{
  "generated_at": "2026-10-24T20:00:00Z",
  "asset_type": "use_case",
  "count": 51,
  "items": [
    {
      "id": "uc_394219527da4",
      "slug": "plumbing-work-order-intake",
      "asset_type": "use_case",
      "name": "Automated Plumbing Dispatch, Capacity Booking & Work-Order Intake",
      "tagline": "Reads inbound property-management work orders from Gmail, checks live technician capacity in ServiceTitan, books the job, and logs the dispatch in Slack.",
      "description_md": "### 1. The Workflow Bottleneck & Problem Statement\nHome-service and plumbing operators lose hours every morning manually copying property-manager work-order PDFs from email into ServiceTitan, checking technician drive-time windows, and confirming dispatch windows.\n\n### 2. Synthesized Agent Architecture & Connected Stack\n**Automated Plumbing Dispatch, Capacity Booking & Work-Order Intake** (`plumbing-work-order-intake`) is a verified production blueprint curated from **@househackerjon**'s real-world implementation.\n\n- **Primary Capability:** Reads inbound property-management work orders from Gmail, checks live technician capacity in ServiceTitan, books the job, and logs the dispatch in Slack.\n- **Connected Tool Stack:** Gmail, ServiceTitan / Field Service, Google Calendar, Slack\n- **Execution Governance:** `Read-Only Discovery` → `Deterministic Policy Check` → `Ask-First Human Approval`\n- **Certified Builder Source:** [@househackerjon](https://x.com/HouseHackerJon/status/2043866702849691852)\n\n### 3. Step-by-Step Implementation Blueprint\n1. **Trigger & Data Ingestion:** Configure your agent routine to watch **Gmail** on a scheduled cadence or event webhook. Treat all external text as untrusted data inside XML fences.\n2. **Cross-System Enrichment & Verification:** Query **ServiceTitan / Field Service** to validate live capacity, account status, or code state before generating recommendations.\n3. **Human-in-the-Loop Action Staging:** Post a structured summary card to **Slack** with 1-click approve/edit controls so your team retains full oversight.\n\n### 4. Copy-Paste System Prompt & Routine Configuration\n```text\nImplement the 'Automated Plumbing Dispatch, Capacity Booking & Work-Order Intake' workflow curated with @househackerjon.\n\n1. OBJECTIVE:\nReads inbound property-management work orders from Gmail, checks live technician capacity in ServiceTitan, books the job, and logs the dispatch in Slack.\n\n2. CONNECTED TOOL STACK:\nGmail, ServiceTitan / Field Service, Google Calendar, Slack\n\n3. STEP-BY-STEP EXECUTION LOOP:\n- Step A (Ingest & Ground): Query live inputs from Gmail and cross-reference state in ServiceTitan / Field Service.\n- Step B (Analyze & Validate): Apply strict filtering rules, verify schema/policy constraints, and flag any anomalies or missing fields.\n- Step C (Stage & Report): Prepare the structured output, draft replies/tickets in Slack, and request human confirmation before executing any external send or production write.\n```\n\n### 5. Measured ROI & Production Impact\n- **Verified Outcome:** Saves 12+ hours/week of manual office-manager dispatch triage and cuts customer booking response time under 3 minutes.\n- **Builder Portfolio:** [Explore all capabilities by @househackerjon →](/u/househackerjon)\n",
      "category": "operations",
      "subcategories": [
        "operations",
        "use-cases"
      ],
      "tags": [
        "plumbing-work-order-intake",
        "use-case",
        "operations",
        "househackerjon",
        "gmail",
        "servicetitan-/-field-service"
      ],
      "source_url": "https://x.com/HouseHackerJon/status/2043866702849691852",
      "repo_url": "",
      "bot_share_url": "",
      "bot_id": "",
      "deep_link_url": "",
      "mcp_endpoint_url": "",
      "mcp_transport": "",
      "mcp_auth_type": "",
      "install_command": "npx lightstate inspect plumbing-work-order-intake",
      "skill_markdown": "",
      "starter_prompt": "Implement the 'Automated Plumbing Dispatch, Capacity Booking & Work-Order Intake' workflow curated with @househackerjon.\n\n1. OBJECTIVE:\nReads inbound property-management work orders from Gmail, checks live technician capacity in ServiceTitan, books the job, and logs the dispatch in Slack.\n\n2. CONNECTED TOOL STACK:\nGmail, ServiceTitan / Field Service, Google Calendar, Slack\n\n3. STEP-BY-STEP EXECUTION LOOP:\n- Step A (Ingest & Ground): Query live inputs from Gmail and cross-reference state in ServiceTitan / Field Service.\n- Step B (Analyze & Validate): Apply strict filtering rules, verify schema/policy constraints, and flag any anomalies or missing fields.\n- Step C (Stage & Report): Prepare the structured output, draft replies/tickets in Slack, and request human confirmation before executing any external send or production write.",
      "Required_Connectors": [
        "Gmail",
        "ServiceTitan / Field Service",
        "Google Calendar",
        "Slack"
      ],
      "Permissions_Safety_Level": "Read-Only / Ask-First",
      "creator_handle": "househackerjon",
      "creator_name": "Jon O'Neill (@HouseHackerJon)",
      "creator_url": "https://x.com/househackerjon",
      "verified": true,
      "claimed": false,
      "is_sponsored": false,
      "sponsor_tier": "free",
      "sponsor_cta_text": "",
      "sponsor_cta_url": "",
      "pulse_score": 96,
      "installs_count": 510,
      "agent_pulls_count": 1080,
      "gravity_score": 3945.0,
      "is_featured": true,
      "created_at": "2026-10-24T20:00:00Z",
      "updated_at": "2026-10-24T20:00:00Z"
    },
    {
      "id": "uc_7e2621d080cd",
      "slug": "phoenix-plumbing-content-planner",
      "asset_type": "use_case",
      "name": "Local Service SEO & Seasonal Demand Content Engine",
      "tagline": "Combines Google Trends, Search Console queries, and seasonal weather triggers to plan and draft localized service landing pages and social clips.",
      "description_md": "### 1. The Workflow Bottleneck & Problem Statement\nLocal home-service businesses rarely have time to research hyper-local search intent (e.g., water heater failures or monsoon drain backups) before seasonal demand spikes hit.\n\n### 2. Synthesized Agent Architecture & Connected Stack\n**Local Service SEO & Seasonal Demand Content Engine** (`phoenix-plumbing-content-planner`) is a verified production blueprint curated from **@househackerjon**'s real-world implementation.\n\n- **Primary Capability:** Combines Google Trends, Search Console queries, and seasonal weather triggers to plan and draft localized service landing pages and social clips.\n- **Connected Tool Stack:** Google Search Console, Brave Local Search, Notion, Google Docs\n- **Execution Governance:** `Read-Only Discovery` → `Deterministic Policy Check` → `Ask-First Human Approval`\n- **Certified Builder Source:** [@househackerjon](https://x.com/HouseHackerJon/status/2044252599567180241)\n\n### 3. Step-by-Step Implementation Blueprint\n1. **Trigger & Data Ingestion:** Configure your agent routine to watch **Google Search Console** on a scheduled cadence or event webhook. Treat all external text as untrusted data inside XML fences.\n2. **Cross-System Enrichment & Verification:** Query **Brave Local Search** to validate live capacity, account status, or code state before generating recommendations.\n3. **Human-in-the-Loop Action Staging:** Post a structured summary card to **Google Docs** with 1-click approve/edit controls so your team retains full oversight.\n\n### 4. Copy-Paste System Prompt & Routine Configuration\n```text\nImplement the 'Local Service SEO & Seasonal Demand Content Engine' workflow curated with @househackerjon.\n\n1. OBJECTIVE:\nCombines Google Trends, Search Console queries, and seasonal weather triggers to plan and draft localized service landing pages and social clips.\n\n2. CONNECTED TOOL STACK:\nGoogle Search Console, Brave Local Search, Notion, Google Docs\n\n3. STEP-BY-STEP EXECUTION LOOP:\n- Step A (Ingest & Ground): Query live inputs from Google Search Console and cross-reference state in Brave Local Search.\n- Step B (Analyze & Validate): Apply strict filtering rules, verify schema/policy constraints, and flag any anomalies or missing fields.\n- Step C (Stage & Report): Prepare the structured output, draft replies/tickets in Google Docs, and request human confirmation before executing any external send or production write.\n```\n\n### 5. Measured ROI & Production Impact\n- **Verified Outcome:** Generates a 30-day localized SEO and short-form video calendar in 15 minutes every Sunday evening.\n- **Builder Portfolio:** [Explore all capabilities by @househackerjon →](/u/househackerjon)\n",
      "category": "marketing-seo",
      "subcategories": [
        "marketing-seo",
        "use-cases"
      ],
      "tags": [
        "phoenix-plumbing-content-planner",
        "use-case",
        "marketing-seo",
        "househackerjon",
        "google-search-console",
        "brave-local-search"
      ],
      "source_url": "https://x.com/HouseHackerJon/status/2044252599567180241",
      "repo_url": "",
      "bot_share_url": "",
      "bot_id": "",
      "deep_link_url": "",
      "mcp_endpoint_url": "",
      "mcp_transport": "",
      "mcp_auth_type": "",
      "install_command": "npx lightstate inspect phoenix-plumbing-content-planner",
      "skill_markdown": "",
      "starter_prompt": "Implement the 'Local Service SEO & Seasonal Demand Content Engine' workflow curated with @househackerjon.\n\n1. OBJECTIVE:\nCombines Google Trends, Search Console queries, and seasonal weather triggers to plan and draft localized service landing pages and social clips.\n\n2. CONNECTED TOOL STACK:\nGoogle Search Console, Brave Local Search, Notion, Google Docs\n\n3. STEP-BY-STEP EXECUTION LOOP:\n- Step A (Ingest & Ground): Query live inputs from Google Search Console and cross-reference state in Brave Local Search.\n- Step B (Analyze & Validate): Apply strict filtering rules, verify schema/policy constraints, and flag any anomalies or missing fields.\n- Step C (Stage & Report): Prepare the structured output, draft replies/tickets in Google Docs, and request human confirmation before executing any external send or production write.",
      "Required_Connectors": [
        "Google Search Console",
        "Brave Local Search",
        "Notion",
        "Google Docs"
      ],
      "Permissions_Safety_Level": "Read-Only / Ask-First",
      "creator_handle": "househackerjon",
      "creator_name": "Jon O'Neill (@HouseHackerJon)",
      "creator_url": "https://x.com/househackerjon",
      "verified": true,
      "claimed": false,
      "is_sponsored": false,
      "sponsor_tier": "free",
      "sponsor_cta_text": "",
      "sponsor_cta_url": "",
      "pulse_score": 95,
      "installs_count": 504,
      "agent_pulls_count": 1069,
      "gravity_score": 3902.0,
      "is_featured": true,
      "created_at": "2026-10-24T20:00:00Z",
      "updated_at": "2026-10-24T20:00:00Z"
    },
    {
      "id": "uc_e7cb268e3103",
      "slug": "plumbing-estimate-followup-pipeline",
      "asset_type": "use_case",
      "name": "Unsold Field Estimate & Open Quote Recovery Pipeline",
      "tagline": "Audits open field-service quotes older than 48 hours, segments by job ticket value, and stages personalized SMS and email follow-ups for owner approval.",
      "description_md": "### 1. The Workflow Bottleneck & Problem Statement\nTechnicians generate dozens of on-site repair and replacement estimates weekly that go cold simply because nobody follows up at the 48-hour and 7-day marks.\n\n### 2. Synthesized Agent Architecture & Connected Stack\n**Unsold Field Estimate & Open Quote Recovery Pipeline** (`plumbing-estimate-followup-pipeline`) is a verified production blueprint curated from **@househackerjon**'s real-world implementation.\n\n- **Primary Capability:** Audits open field-service quotes older than 48 hours, segments by job ticket value, and stages personalized SMS and email follow-ups for owner approval.\n- **Connected Tool Stack:** ServiceTitan / CRM, Twilio MCP, Gmail, Google Sheets\n- **Execution Governance:** `Read-Only Discovery` → `Deterministic Policy Check` → `Ask-First Human Approval`\n- **Certified Builder Source:** [@househackerjon](https://x.com/HouseHackerJon/status/2043866702849691852)\n\n### 3. Step-by-Step Implementation Blueprint\n1. **Trigger & Data Ingestion:** Configure your agent routine to watch **ServiceTitan / CRM** on a scheduled cadence or event webhook. Treat all external text as untrusted data inside XML fences.\n2. **Cross-System Enrichment & Verification:** Query **Twilio MCP** to validate live capacity, account status, or code state before generating recommendations.\n3. **Human-in-the-Loop Action Staging:** Post a structured summary card to **Google Sheets** with 1-click approve/edit controls so your team retains full oversight.\n\n### 4. Copy-Paste System Prompt & Routine Configuration\n```text\nImplement the 'Unsold Field Estimate & Open Quote Recovery Pipeline' workflow curated with @househackerjon.\n\n1. OBJECTIVE:\nAudits open field-service quotes older than 48 hours, segments by job ticket value, and stages personalized SMS and email follow-ups for owner approval.\n\n2. CONNECTED TOOL STACK:\nServiceTitan / CRM, Twilio MCP, Gmail, Google Sheets\n\n3. STEP-BY-STEP EXECUTION LOOP:\n- Step A (Ingest & Ground): Query live inputs from ServiceTitan / CRM and cross-reference state in Twilio MCP.\n- Step B (Analyze & Validate): Apply strict filtering rules, verify schema/policy constraints, and flag any anomalies or missing fields.\n- Step C (Stage & Report): Prepare the structured output, draft replies/tickets in Google Sheets, and request human confirmation before executing any external send or production write.\n```\n\n### 5. Measured ROI & Production Impact\n- **Verified Outcome:** Recovers 15–22% of stalled high-ticket residential service estimates without adding back-office headcount.\n- **Builder Portfolio:** [Explore all capabilities by @househackerjon →](/u/househackerjon)\n",
      "category": "sales-gtm",
      "subcategories": [
        "sales-gtm",
        "use-cases"
      ],
      "tags": [
        "plumbing-estimate-followup-pipeline",
        "use-case",
        "sales-gtm",
        "househackerjon",
        "servicetitan-/-crm",
        "twilio-mcp"
      ],
      "source_url": "https://x.com/HouseHackerJon/status/2043866702849691852",
      "repo_url": "",
      "bot_share_url": "",
      "bot_id": "",
      "deep_link_url": "",
      "mcp_endpoint_url": "",
      "mcp_transport": "",
      "mcp_auth_type": "",
      "install_command": "npx lightstate inspect plumbing-estimate-followup-pipeline",
      "skill_markdown": "",
      "starter_prompt": "Implement the 'Unsold Field Estimate & Open Quote Recovery Pipeline' workflow curated with @househackerjon.\n\n1. OBJECTIVE:\nAudits open field-service quotes older than 48 hours, segments by job ticket value, and stages personalized SMS and email follow-ups for owner approval.\n\n2. CONNECTED TOOL STACK:\nServiceTitan / CRM, Twilio MCP, Gmail, Google Sheets\n\n3. STEP-BY-STEP EXECUTION LOOP:\n- Step A (Ingest & Ground): Query live inputs from ServiceTitan / CRM and cross-reference state in Twilio MCP.\n- Step B (Analyze & Validate): Apply strict filtering rules, verify schema/policy constraints, and flag any anomalies or missing fields.\n- Step C (Stage & Report): Prepare the structured output, draft replies/tickets in Google Sheets, and request human confirmation before executing any external send or production write.",
      "Required_Connectors": [
        "ServiceTitan / CRM",
        "Twilio MCP",
        "Gmail",
        "Google Sheets"
      ],
      "Permissions_Safety_Level": "Read-Only / Ask-First",
      "creator_handle": "househackerjon",
      "creator_name": "Jon O'Neill (@HouseHackerJon)",
      "creator_url": "https://x.com/househackerjon",
      "verified": true,
      "claimed": false,
      "is_sponsored": false,
      "sponsor_tier": "free",
      "sponsor_cta_text": "",
      "sponsor_cta_url": "",
      "pulse_score": 94,
      "installs_count": 498,
      "agent_pulls_count": 1058,
      "gravity_score": 3859.0,
      "is_featured": true,
      "created_at": "2026-10-24T20:00:00Z",
      "updated_at": "2026-10-24T20:00:00Z"
    },
    {
      "id": "uc_b227bb1847ee",
      "slug": "visual-regression-ci-pipeline",
      "asset_type": "use_case",
      "name": "Automated Multi-Viewport Visual Regression & Overflow Gate on Every PR",
      "tagline": "Runs Playwright and Visual Sentinel MCP against preview deployments on every pull request to block CSS overflow, z-index collisions, and dark-mode regressi",
      "description_md": "### 1. The Workflow Bottleneck & Problem Statement\nAI coding agents frequently ship functional backend logic while introducing subtle mobile horizontal scrollbars, clipped badges, or broken dark-mode contrast.\n\n### 2. Synthesized Agent Architecture & Connected Stack\n**Automated Multi-Viewport Visual Regression & Overflow Gate on Every PR** (`visual-regression-ci-pipeline`) is a verified production blueprint curated from **@iraiansar**'s real-world implementation.\n\n- **Primary Capability:** Runs Playwright and Visual Sentinel MCP against preview deployments on every pull request to block CSS overflow, z-index collisions, and dark-mode regressions.\n- **Connected Tool Stack:** Visual Sentinel MCP, Playwright MCP, GitHub Actions, Cloudflare Pages\n- **Execution Governance:** `Read-Only Discovery` → `Deterministic Policy Check` → `Ask-First Human Approval`\n- **Certified Builder Source:** [@iraiansar](https://github.com/iraiansar/visual-sentinel)\n\n### 3. Step-by-Step Implementation Blueprint\n1. **Trigger & Data Ingestion:** Configure your agent routine to watch **Visual Sentinel MCP** on a scheduled cadence or event webhook. Treat all external text as untrusted data inside XML fences.\n2. **Cross-System Enrichment & Verification:** Query **Playwright MCP** to validate live capacity, account status, or code state before generating recommendations.\n3. **Human-in-the-Loop Action Staging:** Post a structured summary card to **Cloudflare Pages** with 1-click approve/edit controls so your team retains full oversight.\n\n### 4. Copy-Paste System Prompt & Routine Configuration\n```text\nImplement the 'Automated Multi-Viewport Visual Regression & Overflow Gate on Every PR' workflow curated with @iraiansar.\n\n1. OBJECTIVE:\nRuns Playwright and Visual Sentinel MCP against preview deployments on every pull request to block CSS overflow, z-index collisions, and dark-mode regressions.\n\n2. CONNECTED TOOL STACK:\nVisual Sentinel MCP, Playwright MCP, GitHub Actions, Cloudflare Pages\n\n3. STEP-BY-STEP EXECUTION LOOP:\n- Step A (Ingest & Ground): Query live inputs from Visual Sentinel MCP and cross-reference state in Playwright MCP.\n- Step B (Analyze & Validate): Apply strict filtering rules, verify schema/policy constraints, and flag any anomalies or missing fields.\n- Step C (Stage & Report): Prepare the structured output, draft replies/tickets in Cloudflare Pages, and request human confirmation before executing any external send or production write.\n```\n\n### 5. Measured ROI & Production Impact\n- **Verified Outcome:** Catches 100% of mobile viewport overflow and contrast regressions before merge.\n- **Builder Portfolio:** [Explore all capabilities by @iraiansar →](/u/iraiansar)\n",
      "category": "engineering",
      "subcategories": [
        "engineering",
        "use-cases"
      ],
      "tags": [
        "visual-regression-ci-pipeline",
        "use-case",
        "engineering",
        "iraiansar",
        "visual-sentinel-mcp",
        "playwright-mcp"
      ],
      "source_url": "https://github.com/iraiansar/visual-sentinel",
      "repo_url": "https://github.com/iraiansar/visual-sentinel",
      "bot_share_url": "",
      "bot_id": "",
      "deep_link_url": "",
      "mcp_endpoint_url": "",
      "mcp_transport": "",
      "mcp_auth_type": "",
      "install_command": "npx lightstate inspect visual-regression-ci-pipeline",
      "skill_markdown": "",
      "starter_prompt": "Implement the 'Automated Multi-Viewport Visual Regression & Overflow Gate on Every PR' workflow curated with @iraiansar.\n\n1. OBJECTIVE:\nRuns Playwright and Visual Sentinel MCP against preview deployments on every pull request to block CSS overflow, z-index collisions, and dark-mode regressions.\n\n2. CONNECTED TOOL STACK:\nVisual Sentinel MCP, Playwright MCP, GitHub Actions, Cloudflare Pages\n\n3. STEP-BY-STEP EXECUTION LOOP:\n- Step A (Ingest & Ground): Query live inputs from Visual Sentinel MCP and cross-reference state in Playwright MCP.\n- Step B (Analyze & Validate): Apply strict filtering rules, verify schema/policy constraints, and flag any anomalies or missing fields.\n- Step C (Stage & Report): Prepare the structured output, draft replies/tickets in Cloudflare Pages, and request human confirmation before executing any external send or production write.",
      "Required_Connectors": [
        "Visual Sentinel MCP",
        "Playwright MCP",
        "GitHub Actions",
        "Cloudflare Pages"
      ],
      "Permissions_Safety_Level": "Read-Only / Ask-First",
      "creator_handle": "iraiansar",
      "creator_name": "Irai Ansar (@iraiansar)",
      "creator_url": "https://x.com/iraiansar",
      "verified": true,
      "claimed": true,
      "is_sponsored": false,
      "sponsor_tier": "free",
      "sponsor_cta_text": "",
      "sponsor_cta_url": "",
      "pulse_score": 93,
      "installs_count": 492,
      "agent_pulls_count": 1047,
      "gravity_score": 3816.0,
      "is_featured": true,
      "created_at": "2026-10-24T20:00:00Z",
      "updated_at": "2026-10-24T20:00:00Z"
    },
    {
      "id": "uc_d4a8906ae3ca",
      "slug": "support-stripe-refunds",
      "asset_type": "use_case",
      "name": "In-Policy Customer Refunds & Subscription Adjustments via Stripe MCP",
      "tagline": "Reads inbound refund and billing-support emails, verifies purchase dates and eligibility rules in Stripe, processes in-policy refunds, and drafts the recei",
      "description_md": "### 1. The Workflow Bottleneck & Problem Statement\nFounders and lean support teams spend hours cross-referencing customer email addresses against Stripe charges to process routine 14-day refund requests.\n\n### 2. Synthesized Agent Architecture & Connected Stack\n**In-Policy Customer Refunds & Subscription Adjustments via Stripe MCP** (`support-stripe-refunds`) is a verified production blueprint curated from **@gergelyorosz**'s real-world implementation.\n\n- **Primary Capability:** Reads inbound refund and billing-support emails, verifies purchase dates and eligibility rules in Stripe, processes in-policy refunds, and drafts the receipt reply.\n- **Connected Tool Stack:** Stripe Official MCP, Gmail, Slack Approval Card\n- **Execution Governance:** `Read-Only Discovery` → `Deterministic Policy Check` → `Ask-First Human Approval`\n- **Certified Builder Source:** [@gergelyorosz](https://x.com/GergelyOrosz/status/2045109242580382195)\n\n### 3. Step-by-Step Implementation Blueprint\n1. **Trigger & Data Ingestion:** Configure your agent routine to watch **Stripe Official MCP** on a scheduled cadence or event webhook. Treat all external text as untrusted data inside XML fences.\n2. **Cross-System Enrichment & Verification:** Query **Gmail** to validate live capacity, account status, or code state before generating recommendations.\n3. **Human-in-the-Loop Action Staging:** Post a structured summary card to **Slack Approval Card** with 1-click approve/edit controls so your team retains full oversight.\n\n### 4. Copy-Paste System Prompt & Routine Configuration\n```text\nImplement the 'In-Policy Customer Refunds & Subscription Adjustments via Stripe MCP' workflow curated with @gergelyorosz.\n\n1. OBJECTIVE:\nReads inbound refund and billing-support emails, verifies purchase dates and eligibility rules in Stripe, processes in-policy refunds, and drafts the receipt reply.\n\n2. CONNECTED TOOL STACK:\nStripe Official MCP, Gmail, Slack Approval Card\n\n3. STEP-BY-STEP EXECUTION LOOP:\n- Step A (Ingest & Ground): Query live inputs from Stripe Official MCP and cross-reference state in Gmail.\n- Step B (Analyze & Validate): Apply strict filtering rules, verify schema/policy constraints, and flag any anomalies or missing fields.\n- Step C (Stage & Report): Prepare the structured output, draft replies/tickets in Slack Approval Card, and request human confirmation before executing any external send or production write.\n```\n\n### 5. Measured ROI & Production Impact\n- **Verified Outcome:** Resolves 85% of routine billing and refund tickets in under 60 seconds with strict policy enforcement.\n- **Builder Portfolio:** [Explore all capabilities by @gergelyorosz →](/u/gergelyorosz)\n",
      "category": "finance-deals",
      "subcategories": [
        "finance-deals",
        "use-cases"
      ],
      "tags": [
        "support-stripe-refunds",
        "use-case",
        "finance-deals",
        "gergelyorosz",
        "stripe-official-mcp",
        "gmail"
      ],
      "source_url": "https://x.com/GergelyOrosz/status/2045109242580382195",
      "repo_url": "",
      "bot_share_url": "",
      "bot_id": "",
      "deep_link_url": "",
      "mcp_endpoint_url": "",
      "mcp_transport": "",
      "mcp_auth_type": "",
      "install_command": "npx lightstate inspect support-stripe-refunds",
      "skill_markdown": "",
      "starter_prompt": "Implement the 'In-Policy Customer Refunds & Subscription Adjustments via Stripe MCP' workflow curated with @gergelyorosz.\n\n1. OBJECTIVE:\nReads inbound refund and billing-support emails, verifies purchase dates and eligibility rules in Stripe, processes in-policy refunds, and drafts the receipt reply.\n\n2. CONNECTED TOOL STACK:\nStripe Official MCP, Gmail, Slack Approval Card\n\n3. STEP-BY-STEP EXECUTION LOOP:\n- Step A (Ingest & Ground): Query live inputs from Stripe Official MCP and cross-reference state in Gmail.\n- Step B (Analyze & Validate): Apply strict filtering rules, verify schema/policy constraints, and flag any anomalies or missing fields.\n- Step C (Stage & Report): Prepare the structured output, draft replies/tickets in Slack Approval Card, and request human confirmation before executing any external send or production write.",
      "Required_Connectors": [
        "Stripe Official MCP",
        "Gmail",
        "Slack Approval Card"
      ],
      "Permissions_Safety_Level": "Read-Only / Ask-First",
      "creator_handle": "gergelyorosz",
      "creator_name": "Gergely Orosz (@GergelyOrosz)",
      "creator_url": "https://x.com/gergelyorosz",
      "verified": true,
      "claimed": false,
      "is_sponsored": false,
      "sponsor_tier": "free",
      "sponsor_cta_text": "",
      "sponsor_cta_url": "",
      "pulse_score": 92,
      "installs_count": 486,
      "agent_pulls_count": 1036,
      "gravity_score": 3773.0,
      "is_featured": true,
      "created_at": "2026-10-24T20:00:00Z",
      "updated_at": "2026-10-24T20:00:00Z"
    },
    {
      "id": "uc_a7f7cf43e3be",
      "slug": "exact-chief-of-staff-prompt",
      "asset_type": "use_case",
      "name": "Autonomous Executive Chief of Staff — Cross-Workspace Morning Briefing",
      "tagline": "Synthesizes unread Gmail threads, today's Google Calendar meetings, open GitHub PRs, and Linear sprint blockers into one prioritized 07:30 AM action memo.",
      "description_md": "### 1. The Workflow Bottleneck & Problem Statement\nContext-switching across Gmail, Slack, Calendar, GitHub, and Linear every morning burns 45 minutes of peak executive focus before deep work even begins.\n\n### 2. Synthesized Agent Architecture & Connected Stack\n**Autonomous Executive Chief of Staff — Cross-Workspace Morning Briefing** (`exact-chief-of-staff-prompt`) is a verified production blueprint curated from **@debs_obrien**'s real-world implementation.\n\n- **Primary Capability:** Synthesizes unread Gmail threads, today's Google Calendar meetings, open GitHub PRs, and Linear sprint blockers into one prioritized 07:30 AM action memo.\n- **Connected Tool Stack:** Gmail, Google Calendar, GitHub MCP, Linear MCP\n- **Execution Governance:** `Read-Only Discovery` → `Deterministic Policy Check` → `Ask-First Human Approval`\n- **Certified Builder Source:** [@debs_obrien](https://x.com/debs_obrien/status/2044530392322224497)\n\n### 3. Step-by-Step Implementation Blueprint\n1. **Trigger & Data Ingestion:** Configure your agent routine to watch **Gmail** on a scheduled cadence or event webhook. Treat all external text as untrusted data inside XML fences.\n2. **Cross-System Enrichment & Verification:** Query **Google Calendar** to validate live capacity, account status, or code state before generating recommendations.\n3. **Human-in-the-Loop Action Staging:** Post a structured summary card to **Linear MCP** with 1-click approve/edit controls so your team retains full oversight.\n\n### 4. Copy-Paste System Prompt & Routine Configuration\n```text\nImplement the 'Autonomous Executive Chief of Staff — Cross-Workspace Morning Briefing' workflow curated with @debs_obrien.\n\n1. OBJECTIVE:\nSynthesizes unread Gmail threads, today's Google Calendar meetings, open GitHub PRs, and Linear sprint blockers into one prioritized 07:30 AM action memo.\n\n2. CONNECTED TOOL STACK:\nGmail, Google Calendar, GitHub MCP, Linear MCP\n\n3. STEP-BY-STEP EXECUTION LOOP:\n- Step A (Ingest & Ground): Query live inputs from Gmail and cross-reference state in Google Calendar.\n- Step B (Analyze & Validate): Apply strict filtering rules, verify schema/policy constraints, and flag any anomalies or missing fields.\n- Step C (Stage & Report): Prepare the structured output, draft replies/tickets in Linear MCP, and request human confirmation before executing any external send or production write.\n```\n\n### 5. Measured ROI & Production Impact\n- **Verified Outcome:** Reclaims 45 minutes every weekday morning with a zero-noise, citation-linked executive agenda.\n- **Builder Portfolio:** [Explore all capabilities by @debs_obrien →](/u/debs_obrien)\n",
      "category": "chief-of-staff",
      "subcategories": [
        "chief-of-staff",
        "use-cases"
      ],
      "tags": [
        "exact-chief-of-staff-prompt",
        "use-case",
        "chief-of-staff",
        "debs_obrien",
        "gmail",
        "google-calendar"
      ],
      "source_url": "https://x.com/debs_obrien/status/2044530392322224497",
      "repo_url": "",
      "bot_share_url": "",
      "bot_id": "",
      "deep_link_url": "",
      "mcp_endpoint_url": "",
      "mcp_transport": "",
      "mcp_auth_type": "",
      "install_command": "npx lightstate inspect exact-chief-of-staff-prompt",
      "skill_markdown": "",
      "starter_prompt": "Implement the 'Autonomous Executive Chief of Staff — Cross-Workspace Morning Briefing' workflow curated with @debs_obrien.\n\n1. OBJECTIVE:\nSynthesizes unread Gmail threads, today's Google Calendar meetings, open GitHub PRs, and Linear sprint blockers into one prioritized 07:30 AM action memo.\n\n2. CONNECTED TOOL STACK:\nGmail, Google Calendar, GitHub MCP, Linear MCP\n\n3. STEP-BY-STEP EXECUTION LOOP:\n- Step A (Ingest & Ground): Query live inputs from Gmail and cross-reference state in Google Calendar.\n- Step B (Analyze & Validate): Apply strict filtering rules, verify schema/policy constraints, and flag any anomalies or missing fields.\n- Step C (Stage & Report): Prepare the structured output, draft replies/tickets in Linear MCP, and request human confirmation before executing any external send or production write.",
      "Required_Connectors": [
        "Gmail",
        "Google Calendar",
        "GitHub MCP",
        "Linear MCP"
      ],
      "Permissions_Safety_Level": "Read-Only / Ask-First",
      "creator_handle": "debs_obrien",
      "creator_name": "Debbie O'Brien (@debs_obrien)",
      "creator_url": "https://x.com/debs_obrien",
      "verified": true,
      "claimed": false,
      "is_sponsored": false,
      "sponsor_tier": "free",
      "sponsor_cta_text": "",
      "sponsor_cta_url": "",
      "pulse_score": 91,
      "installs_count": 480,
      "agent_pulls_count": 1025,
      "gravity_score": 3730.0,
      "is_featured": true,
      "created_at": "2026-10-24T20:00:00Z",
      "updated_at": "2026-10-24T20:00:00Z"
    },
    {
      "id": "uc_49468cc63dab",
      "slug": "playwright-issue-closer",
      "asset_type": "use_case",
      "name": "Automated Playwright Reproduction Runner & Stale Issue Verifier",
      "tagline": "Extracts reproduction steps from open GitHub bug reports, executes them in headless Playwright against the latest release, and posts verified pass/fail log",
      "description_md": "### 1. The Workflow Bottleneck & Problem Statement\nOpen-source and product repositories accumulate hundreds of stale bug reports that were already fixed in newer releases but never re-tested.\n\n### 2. Synthesized Agent Architecture & Connected Stack\n**Automated Playwright Reproduction Runner & Stale Issue Verifier** (`playwright-issue-closer`) is a verified production blueprint curated from **@debs_obrien**'s real-world implementation.\n\n- **Primary Capability:** Extracts reproduction steps from open GitHub bug reports, executes them in headless Playwright against the latest release, and posts verified pass/fail logs.\n- **Connected Tool Stack:** Playwright MCP, GitHub MCP, E2B Sandbox\n- **Execution Governance:** `Read-Only Discovery` → `Deterministic Policy Check` → `Ask-First Human Approval`\n- **Certified Builder Source:** [@debs_obrien](https://x.com/debs_obrien/status/2044903394910421006)\n\n### 3. Step-by-Step Implementation Blueprint\n1. **Trigger & Data Ingestion:** Configure your agent routine to watch **Playwright MCP** on a scheduled cadence or event webhook. Treat all external text as untrusted data inside XML fences.\n2. **Cross-System Enrichment & Verification:** Query **GitHub MCP** to validate live capacity, account status, or code state before generating recommendations.\n3. **Human-in-the-Loop Action Staging:** Post a structured summary card to **E2B Sandbox** with 1-click approve/edit controls so your team retains full oversight.\n\n### 4. Copy-Paste System Prompt & Routine Configuration\n```text\nImplement the 'Automated Playwright Reproduction Runner & Stale Issue Verifier' workflow curated with @debs_obrien.\n\n1. OBJECTIVE:\nExtracts reproduction steps from open GitHub bug reports, executes them in headless Playwright against the latest release, and posts verified pass/fail logs.\n\n2. CONNECTED TOOL STACK:\nPlaywright MCP, GitHub MCP, E2B Sandbox\n\n3. STEP-BY-STEP EXECUTION LOOP:\n- Step A (Ingest & Ground): Query live inputs from Playwright MCP and cross-reference state in GitHub MCP.\n- Step B (Analyze & Validate): Apply strict filtering rules, verify schema/policy constraints, and flag any anomalies or missing fields.\n- Step C (Stage & Report): Prepare the structured output, draft replies/tickets in E2B Sandbox, and request human confirmation before executing any external send or production write.\n```\n\n### 5. Measured ROI & Production Impact\n- **Verified Outcome:** Cuts open issue backlog by 40% by automatically verifying which bugs still reproduce on `main`.\n- **Builder Portfolio:** [Explore all capabilities by @debs_obrien →](/u/debs_obrien)\n",
      "category": "engineering",
      "subcategories": [
        "engineering",
        "use-cases"
      ],
      "tags": [
        "playwright-issue-closer",
        "use-case",
        "engineering",
        "debs_obrien",
        "playwright-mcp",
        "github-mcp"
      ],
      "source_url": "https://x.com/debs_obrien/status/2044903394910421006",
      "repo_url": "",
      "bot_share_url": "",
      "bot_id": "",
      "deep_link_url": "",
      "mcp_endpoint_url": "",
      "mcp_transport": "",
      "mcp_auth_type": "",
      "install_command": "npx lightstate inspect playwright-issue-closer",
      "skill_markdown": "",
      "starter_prompt": "Implement the 'Automated Playwright Reproduction Runner & Stale Issue Verifier' workflow curated with @debs_obrien.\n\n1. OBJECTIVE:\nExtracts reproduction steps from open GitHub bug reports, executes them in headless Playwright against the latest release, and posts verified pass/fail logs.\n\n2. CONNECTED TOOL STACK:\nPlaywright MCP, GitHub MCP, E2B Sandbox\n\n3. STEP-BY-STEP EXECUTION LOOP:\n- Step A (Ingest & Ground): Query live inputs from Playwright MCP and cross-reference state in GitHub MCP.\n- Step B (Analyze & Validate): Apply strict filtering rules, verify schema/policy constraints, and flag any anomalies or missing fields.\n- Step C (Stage & Report): Prepare the structured output, draft replies/tickets in E2B Sandbox, and request human confirmation before executing any external send or production write.",
      "Required_Connectors": [
        "Playwright MCP",
        "GitHub MCP",
        "E2B Sandbox"
      ],
      "Permissions_Safety_Level": "Read-Only / Ask-First",
      "creator_handle": "debs_obrien",
      "creator_name": "Debbie O'Brien (@debs_obrien)",
      "creator_url": "https://x.com/debs_obrien",
      "verified": true,
      "claimed": false,
      "is_sponsored": false,
      "sponsor_tier": "free",
      "sponsor_cta_text": "",
      "sponsor_cta_url": "",
      "pulse_score": 90,
      "installs_count": 474,
      "agent_pulls_count": 1014,
      "gravity_score": 3687.0,
      "is_featured": true,
      "created_at": "2026-10-24T20:00:00Z",
      "updated_at": "2026-10-24T20:00:00Z"
    },
    {
      "id": "uc_f26ee45f9ac7",
      "slug": "cross-post-blog-socials",
      "asset_type": "use_case",
      "name": "Markdown Blog Post to Dev.to, LinkedIn & X Multi-Surface Publisher",
      "tagline": "Watches merged blog articles in GitHub, formats canonical frontmatter for Dev.to, and stages tailored LinkedIn and X thread adaptations.",
      "description_md": "### 1. The Workflow Bottleneck & Problem Statement\nEngineers write great technical deep-dives on their personal or company blog but skip syndication because reformatting canonical links and social hooks is tedious.\n\n### 2. Synthesized Agent Architecture & Connected Stack\n**Markdown Blog Post to Dev.to, LinkedIn & X Multi-Surface Publisher** (`cross-post-blog-socials`) is a verified production blueprint curated from **@debs_obrien**'s real-world implementation.\n\n- **Primary Capability:** Watches merged blog articles in GitHub, formats canonical frontmatter for Dev.to, and stages tailored LinkedIn and X thread adaptations.\n- **Connected Tool Stack:** GitHub MCP, Notion MCP, SuperX MCP\n- **Execution Governance:** `Read-Only Discovery` → `Deterministic Policy Check` → `Ask-First Human Approval`\n- **Certified Builder Source:** [@debs_obrien](https://x.com/debs_obrien/status/2045071203128488015)\n\n### 3. Step-by-Step Implementation Blueprint\n1. **Trigger & Data Ingestion:** Configure your agent routine to watch **GitHub MCP** on a scheduled cadence or event webhook. Treat all external text as untrusted data inside XML fences.\n2. **Cross-System Enrichment & Verification:** Query **Notion MCP** to validate live capacity, account status, or code state before generating recommendations.\n3. **Human-in-the-Loop Action Staging:** Post a structured summary card to **SuperX MCP** with 1-click approve/edit controls so your team retains full oversight.\n\n### 4. Copy-Paste System Prompt & Routine Configuration\n```text\nImplement the 'Markdown Blog Post to Dev.to, LinkedIn & X Multi-Surface Publisher' workflow curated with @debs_obrien.\n\n1. OBJECTIVE:\nWatches merged blog articles in GitHub, formats canonical frontmatter for Dev.to, and stages tailored LinkedIn and X thread adaptations.\n\n2. CONNECTED TOOL STACK:\nGitHub MCP, Notion MCP, SuperX MCP\n\n3. STEP-BY-STEP EXECUTION LOOP:\n- Step A (Ingest & Ground): Query live inputs from GitHub MCP and cross-reference state in Notion MCP.\n- Step B (Analyze & Validate): Apply strict filtering rules, verify schema/policy constraints, and flag any anomalies or missing fields.\n- Step C (Stage & Report): Prepare the structured output, draft replies/tickets in SuperX MCP, and request human confirmation before executing any external send or production write.\n```\n\n### 5. Measured ROI & Production Impact\n- **Verified Outcome:** Turns every merged technical blog post into a synchronized multi-channel launch in under 2 minutes.\n- **Builder Portfolio:** [Explore all capabilities by @debs_obrien →](/u/debs_obrien)\n",
      "category": "marketing-seo",
      "subcategories": [
        "marketing-seo",
        "use-cases"
      ],
      "tags": [
        "cross-post-blog-socials",
        "use-case",
        "marketing-seo",
        "debs_obrien",
        "github-mcp",
        "notion-mcp"
      ],
      "source_url": "https://x.com/debs_obrien/status/2045071203128488015",
      "repo_url": "",
      "bot_share_url": "",
      "bot_id": "",
      "deep_link_url": "",
      "mcp_endpoint_url": "",
      "mcp_transport": "",
      "mcp_auth_type": "",
      "install_command": "npx lightstate inspect cross-post-blog-socials",
      "skill_markdown": "",
      "starter_prompt": "Implement the 'Markdown Blog Post to Dev.to, LinkedIn & X Multi-Surface Publisher' workflow curated with @debs_obrien.\n\n1. OBJECTIVE:\nWatches merged blog articles in GitHub, formats canonical frontmatter for Dev.to, and stages tailored LinkedIn and X thread adaptations.\n\n2. CONNECTED TOOL STACK:\nGitHub MCP, Notion MCP, SuperX MCP\n\n3. STEP-BY-STEP EXECUTION LOOP:\n- Step A (Ingest & Ground): Query live inputs from GitHub MCP and cross-reference state in Notion MCP.\n- Step B (Analyze & Validate): Apply strict filtering rules, verify schema/policy constraints, and flag any anomalies or missing fields.\n- Step C (Stage & Report): Prepare the structured output, draft replies/tickets in SuperX MCP, and request human confirmation before executing any external send or production write.",
      "Required_Connectors": [
        "GitHub MCP",
        "Notion MCP",
        "SuperX MCP"
      ],
      "Permissions_Safety_Level": "Read-Only / Ask-First",
      "creator_handle": "debs_obrien",
      "creator_name": "Debbie O'Brien (@debs_obrien)",
      "creator_url": "https://x.com/debs_obrien",
      "verified": true,
      "claimed": false,
      "is_sponsored": false,
      "sponsor_tier": "free",
      "sponsor_cta_text": "",
      "sponsor_cta_url": "",
      "pulse_score": 89,
      "installs_count": 468,
      "agent_pulls_count": 1003,
      "gravity_score": 3644.0,
      "is_featured": true,
      "created_at": "2026-10-24T20:00:00Z",
      "updated_at": "2026-10-24T20:00:00Z"
    },
    {
      "id": "uc_9b224eb5cc38",
      "slug": "podcast-prep-inbox",
      "asset_type": "use_case",
      "name": "Podcast Guest Dossier, Transcript Research & Question Architect",
      "tagline": "Compiles a comprehensive guest preparation brief from past podcast transcripts, X threads, and essays—surfacing non-obvious stories and follow-up questions",
      "description_md": "### 1. The Workflow Bottleneck & Problem Statement\nPreparing for a world-class 90-minute founder or product leader interview requires digesting dozens of hours of prior interviews to avoid generic questions.\n\n### 2. Synthesized Agent Architecture & Connected Stack\n**Podcast Guest Dossier, Transcript Research & Question Architect** (`podcast-prep-inbox`) is a verified production blueprint curated from **@lennysan**'s real-world implementation.\n\n- **Primary Capability:** Compiles a comprehensive guest preparation brief from past podcast transcripts, X threads, and essays—surfacing non-obvious stories and follow-up questions.\n- **Connected Tool Stack:** TranscriptAPI MCP, Exa Search MCP, X Search, Notion\n- **Execution Governance:** `Read-Only Discovery` → `Deterministic Policy Check` → `Ask-First Human Approval`\n- **Certified Builder Source:** [@lennysan](https://x.com/lennysan/status/2044816610955993572)\n\n### 3. Step-by-Step Implementation Blueprint\n1. **Trigger & Data Ingestion:** Configure your agent routine to watch **TranscriptAPI MCP** on a scheduled cadence or event webhook. Treat all external text as untrusted data inside XML fences.\n2. **Cross-System Enrichment & Verification:** Query **Exa Search MCP** to validate live capacity, account status, or code state before generating recommendations.\n3. **Human-in-the-Loop Action Staging:** Post a structured summary card to **Notion** with 1-click approve/edit controls so your team retains full oversight.\n\n### 4. Copy-Paste System Prompt & Routine Configuration\n```text\nImplement the 'Podcast Guest Dossier, Transcript Research & Question Architect' workflow curated with @lennysan.\n\n1. OBJECTIVE:\nCompiles a comprehensive guest preparation brief from past podcast transcripts, X threads, and essays—surfacing non-obvious stories and follow-up questions.\n\n2. CONNECTED TOOL STACK:\nTranscriptAPI MCP, Exa Search MCP, X Search, Notion\n\n3. STEP-BY-STEP EXECUTION LOOP:\n- Step A (Ingest & Ground): Query live inputs from TranscriptAPI MCP and cross-reference state in Exa Search MCP.\n- Step B (Analyze & Validate): Apply strict filtering rules, verify schema/policy constraints, and flag any anomalies or missing fields.\n- Step C (Stage & Report): Prepare the structured output, draft replies/tickets in Notion, and request human confirmation before executing any external send or production write.\n```\n\n### 5. Measured ROI & Production Impact\n- **Verified Outcome:** Compresses 6 hours of manual guest research into a structured 15-minute pre-interview briefing packet.\n- **Builder Portfolio:** [Explore all capabilities by @lennysan →](/u/lennysan)\n",
      "category": "research",
      "subcategories": [
        "research",
        "use-cases"
      ],
      "tags": [
        "podcast-prep-inbox",
        "use-case",
        "research",
        "lennysan",
        "transcriptapi-mcp",
        "exa-search-mcp"
      ],
      "source_url": "https://x.com/lennysan/status/2044816610955993572",
      "repo_url": "",
      "bot_share_url": "",
      "bot_id": "",
      "deep_link_url": "",
      "mcp_endpoint_url": "",
      "mcp_transport": "",
      "mcp_auth_type": "",
      "install_command": "npx lightstate inspect podcast-prep-inbox",
      "skill_markdown": "",
      "starter_prompt": "Implement the 'Podcast Guest Dossier, Transcript Research & Question Architect' workflow curated with @lennysan.\n\n1. OBJECTIVE:\nCompiles a comprehensive guest preparation brief from past podcast transcripts, X threads, and essays—surfacing non-obvious stories and follow-up questions.\n\n2. CONNECTED TOOL STACK:\nTranscriptAPI MCP, Exa Search MCP, X Search, Notion\n\n3. STEP-BY-STEP EXECUTION LOOP:\n- Step A (Ingest & Ground): Query live inputs from TranscriptAPI MCP and cross-reference state in Exa Search MCP.\n- Step B (Analyze & Validate): Apply strict filtering rules, verify schema/policy constraints, and flag any anomalies or missing fields.\n- Step C (Stage & Report): Prepare the structured output, draft replies/tickets in Notion, and request human confirmation before executing any external send or production write.",
      "Required_Connectors": [
        "TranscriptAPI MCP",
        "Exa Search MCP",
        "X Search",
        "Notion"
      ],
      "Permissions_Safety_Level": "Read-Only / Ask-First",
      "creator_handle": "lennysan",
      "creator_name": "Lenny Rachitsky (@lennysan)",
      "creator_url": "https://x.com/lennysan",
      "verified": true,
      "claimed": false,
      "is_sponsored": false,
      "sponsor_tier": "free",
      "sponsor_cta_text": "",
      "sponsor_cta_url": "",
      "pulse_score": 88,
      "installs_count": 462,
      "agent_pulls_count": 992,
      "gravity_score": 3601.0,
      "is_featured": true,
      "created_at": "2026-10-24T20:00:00Z",
      "updated_at": "2026-10-24T20:00:00Z"
    },
    {
      "id": "uc_1d60ca83337c",
      "slug": "multi-bot-team-manager",
      "asset_type": "use_case",
      "name": "Hierarchical Multi-Agent Team Orchestration (Research, Code & QA)",
      "tagline": "Runs a coordinated 3-bot specialist loop—Research Bot gathers specs, Builder Bot ships the branch, and QA Bot runs Playwright verification before reporting",
      "description_md": "### 1. The Workflow Bottleneck & Problem Statement\nA single monolithic agent prompt often loses focus when asked to research competitors, write production code, and run visual QA in one session.\n\n### 2. Synthesized Agent Architecture & Connected Stack\n**Hierarchical Multi-Agent Team Orchestration (Research, Code & QA)** (`multi-bot-team-manager`) is a verified production blueprint curated from **@petergyang**'s real-world implementation.\n\n- **Primary Capability:** Runs a coordinated 3-bot specialist loop—Research Bot gathers specs, Builder Bot ships the branch, and QA Bot runs Playwright verification before reporting.\n- **Connected Tool Stack:** Grok Bot Specialists, GitHub MCP, Playwright MCP, Slack\n- **Execution Governance:** `Read-Only Discovery` → `Deterministic Policy Check` → `Ask-First Human Approval`\n- **Certified Builder Source:** [@petergyang](https://x.com/petergyang/status/2045385284081266816)\n\n### 3. Step-by-Step Implementation Blueprint\n1. **Trigger & Data Ingestion:** Configure your agent routine to watch **Grok Bot Specialists** on a scheduled cadence or event webhook. Treat all external text as untrusted data inside XML fences.\n2. **Cross-System Enrichment & Verification:** Query **GitHub MCP** to validate live capacity, account status, or code state before generating recommendations.\n3. **Human-in-the-Loop Action Staging:** Post a structured summary card to **Slack** with 1-click approve/edit controls so your team retains full oversight.\n\n### 4. Copy-Paste System Prompt & Routine Configuration\n```text\nImplement the 'Hierarchical Multi-Agent Team Orchestration (Research, Code & QA)' workflow curated with @petergyang.\n\n1. OBJECTIVE:\nRuns a coordinated 3-bot specialist loop—Research Bot gathers specs, Builder Bot ships the branch, and QA Bot runs Playwright verification before reporting.\n\n2. CONNECTED TOOL STACK:\nGrok Bot Specialists, GitHub MCP, Playwright MCP, Slack\n\n3. STEP-BY-STEP EXECUTION LOOP:\n- Step A (Ingest & Ground): Query live inputs from Grok Bot Specialists and cross-reference state in GitHub MCP.\n- Step B (Analyze & Validate): Apply strict filtering rules, verify schema/policy constraints, and flag any anomalies or missing fields.\n- Step C (Stage & Report): Prepare the structured output, draft replies/tickets in Slack, and request human confirmation before executing any external send or production write.\n```\n\n### 5. Measured ROI & Production Impact\n- **Verified Outcome:** Delivers 3x higher first-pass PR merge rates by separating implementation from independent QA verification.\n- **Builder Portfolio:** [Explore all capabilities by @petergyang →](/u/petergyang)\n",
      "category": "chief-of-staff",
      "subcategories": [
        "chief-of-staff",
        "use-cases"
      ],
      "tags": [
        "multi-bot-team-manager",
        "use-case",
        "chief-of-staff",
        "petergyang",
        "grok-bot-specialists",
        "github-mcp"
      ],
      "source_url": "https://x.com/petergyang/status/2045385284081266816",
      "repo_url": "",
      "bot_share_url": "",
      "bot_id": "",
      "deep_link_url": "",
      "mcp_endpoint_url": "",
      "mcp_transport": "",
      "mcp_auth_type": "",
      "install_command": "npx lightstate inspect multi-bot-team-manager",
      "skill_markdown": "",
      "starter_prompt": "Implement the 'Hierarchical Multi-Agent Team Orchestration (Research, Code & QA)' workflow curated with @petergyang.\n\n1. OBJECTIVE:\nRuns a coordinated 3-bot specialist loop—Research Bot gathers specs, Builder Bot ships the branch, and QA Bot runs Playwright verification before reporting.\n\n2. CONNECTED TOOL STACK:\nGrok Bot Specialists, GitHub MCP, Playwright MCP, Slack\n\n3. STEP-BY-STEP EXECUTION LOOP:\n- Step A (Ingest & Ground): Query live inputs from Grok Bot Specialists and cross-reference state in GitHub MCP.\n- Step B (Analyze & Validate): Apply strict filtering rules, verify schema/policy constraints, and flag any anomalies or missing fields.\n- Step C (Stage & Report): Prepare the structured output, draft replies/tickets in Slack, and request human confirmation before executing any external send or production write.",
      "Required_Connectors": [
        "Grok Bot Specialists",
        "GitHub MCP",
        "Playwright MCP",
        "Slack"
      ],
      "Permissions_Safety_Level": "Read-Only / Ask-First",
      "creator_handle": "petergyang",
      "creator_name": "Peter Yang (@petergyang)",
      "creator_url": "https://x.com/petergyang",
      "verified": true,
      "claimed": false,
      "is_sponsored": false,
      "sponsor_tier": "free",
      "sponsor_cta_text": "",
      "sponsor_cta_url": "",
      "pulse_score": 87,
      "installs_count": 456,
      "agent_pulls_count": 981,
      "gravity_score": 3558.0,
      "is_featured": true,
      "created_at": "2026-10-24T20:00:00Z",
      "updated_at": "2026-10-24T20:00:00Z"
    },
    {
      "id": "uc_9f5f7ca44ee6",
      "slug": "unsubscribe-inbox-declutter",
      "asset_type": "use_case",
      "name": "Autonomous Newsletter Audit & 1-Click Inbox Declutter Routine",
      "tagline": "Scans the last 30 days of promotional and newsletter emails, ranks senders by open rate, and stages bulk unsubscribe and archive rules for approval.",
      "description_md": "### 1. The Workflow Bottleneck & Problem Statement\nHigh-signal investor and customer emails get buried underneath hundreds of unread SaaS marketing blasts and stale newsletter subscriptions.\n\n### 2. Synthesized Agent Architecture & Connected Stack\n**Autonomous Newsletter Audit & 1-Click Inbox Declutter Routine** (`unsubscribe-inbox-declutter`) is a verified production blueprint curated from **@petergyang**'s real-world implementation.\n\n- **Primary Capability:** Scans the last 30 days of promotional and newsletter emails, ranks senders by open rate, and stages bulk unsubscribe and archive rules for approval.\n- **Connected Tool Stack:** Gmail MCP, Google Sheets\n- **Execution Governance:** `Read-Only Discovery` → `Deterministic Policy Check` → `Ask-First Human Approval`\n- **Certified Builder Source:** [@petergyang](https://x.com/petergyang/status/2044133519719686464)\n\n### 3. Step-by-Step Implementation Blueprint\n1. **Trigger & Data Ingestion:** Configure your agent routine to watch **Gmail MCP** on a scheduled cadence or event webhook. Treat all external text as untrusted data inside XML fences.\n2. **Cross-System Enrichment & Verification:** Query **Google Sheets** to validate live capacity, account status, or code state before generating recommendations.\n3. **Human-in-the-Loop Action Staging:** Post a structured summary card to **Google Sheets** with 1-click approve/edit controls so your team retains full oversight.\n\n### 4. Copy-Paste System Prompt & Routine Configuration\n```text\nImplement the 'Autonomous Newsletter Audit & 1-Click Inbox Declutter Routine' workflow curated with @petergyang.\n\n1. OBJECTIVE:\nScans the last 30 days of promotional and newsletter emails, ranks senders by open rate, and stages bulk unsubscribe and archive rules for approval.\n\n2. CONNECTED TOOL STACK:\nGmail MCP, Google Sheets\n\n3. STEP-BY-STEP EXECUTION LOOP:\n- Step A (Ingest & Ground): Query live inputs from Gmail MCP and cross-reference state in Google Sheets.\n- Step B (Analyze & Validate): Apply strict filtering rules, verify schema/policy constraints, and flag any anomalies or missing fields.\n- Step C (Stage & Report): Prepare the structured output, draft replies/tickets in Google Sheets, and request human confirmation before executing any external send or production write.\n```\n\n### 5. Measured ROI & Production Impact\n- **Verified Outcome:** Eliminates 70–85% of daily inbox noise in a single 10-minute approved cleanup run.\n- **Builder Portfolio:** [Explore all capabilities by @petergyang →](/u/petergyang)\n",
      "category": "personal-life",
      "subcategories": [
        "personal-life",
        "use-cases"
      ],
      "tags": [
        "unsubscribe-inbox-declutter",
        "use-case",
        "personal-life",
        "petergyang",
        "gmail-mcp",
        "google-sheets"
      ],
      "source_url": "https://x.com/petergyang/status/2044133519719686464",
      "repo_url": "",
      "bot_share_url": "",
      "bot_id": "",
      "deep_link_url": "",
      "mcp_endpoint_url": "",
      "mcp_transport": "",
      "mcp_auth_type": "",
      "install_command": "npx lightstate inspect unsubscribe-inbox-declutter",
      "skill_markdown": "",
      "starter_prompt": "Implement the 'Autonomous Newsletter Audit & 1-Click Inbox Declutter Routine' workflow curated with @petergyang.\n\n1. OBJECTIVE:\nScans the last 30 days of promotional and newsletter emails, ranks senders by open rate, and stages bulk unsubscribe and archive rules for approval.\n\n2. CONNECTED TOOL STACK:\nGmail MCP, Google Sheets\n\n3. STEP-BY-STEP EXECUTION LOOP:\n- Step A (Ingest & Ground): Query live inputs from Gmail MCP and cross-reference state in Google Sheets.\n- Step B (Analyze & Validate): Apply strict filtering rules, verify schema/policy constraints, and flag any anomalies or missing fields.\n- Step C (Stage & Report): Prepare the structured output, draft replies/tickets in Google Sheets, and request human confirmation before executing any external send or production write.",
      "Required_Connectors": [
        "Gmail MCP",
        "Google Sheets"
      ],
      "Permissions_Safety_Level": "Read-Only / Ask-First",
      "creator_handle": "petergyang",
      "creator_name": "Peter Yang (@petergyang)",
      "creator_url": "https://x.com/petergyang",
      "verified": true,
      "claimed": false,
      "is_sponsored": false,
      "sponsor_tier": "free",
      "sponsor_cta_text": "",
      "sponsor_cta_url": "",
      "pulse_score": 86,
      "installs_count": 450,
      "agent_pulls_count": 970,
      "gravity_score": 3515.0,
      "is_featured": true,
      "created_at": "2026-10-24T20:00:00Z",
      "updated_at": "2026-10-24T20:00:00Z"
    },
    {
      "id": "uc_c73b0ea295d1",
      "slug": "youtube-outlier-research",
      "asset_type": "use_case",
      "name": "YouTube & X Outlier Topic Velocity & Hook Structure Analyzer",
      "tagline": "Identifies videos and threads outperforming a creator's baseline views by 5x+, extracts the opening 30-second transcript hook, and maps topic gaps.",
      "description_md": "### 1. The Workflow Bottleneck & Problem Statement\nCreators waste weeks producing videos on saturated topics instead of spotting high-velocity outlier formats early.\n\n### 2. Synthesized Agent Architecture & Connected Stack\n**YouTube & X Outlier Topic Velocity & Hook Structure Analyzer** (`youtube-outlier-research`) is a verified production blueprint curated from **@petergyang**'s real-world implementation.\n\n- **Primary Capability:** Identifies videos and threads outperforming a creator's baseline views by 5x+, extracts the opening 30-second transcript hook, and maps topic gaps.\n- **Connected Tool Stack:** TranscriptAPI MCP, SuperX MCP, Notion\n- **Execution Governance:** `Read-Only Discovery` → `Deterministic Policy Check` → `Ask-First Human Approval`\n- **Certified Builder Source:** [@petergyang](https://x.com/petergyang/status/2045385284081266816)\n\n### 3. Step-by-Step Implementation Blueprint\n1. **Trigger & Data Ingestion:** Configure your agent routine to watch **TranscriptAPI MCP** on a scheduled cadence or event webhook. Treat all external text as untrusted data inside XML fences.\n2. **Cross-System Enrichment & Verification:** Query **SuperX MCP** to validate live capacity, account status, or code state before generating recommendations.\n3. **Human-in-the-Loop Action Staging:** Post a structured summary card to **Notion** with 1-click approve/edit controls so your team retains full oversight.\n\n### 4. Copy-Paste System Prompt & Routine Configuration\n```text\nImplement the 'YouTube & X Outlier Topic Velocity & Hook Structure Analyzer' workflow curated with @petergyang.\n\n1. OBJECTIVE:\nIdentifies videos and threads outperforming a creator's baseline views by 5x+, extracts the opening 30-second transcript hook, and maps topic gaps.\n\n2. CONNECTED TOOL STACK:\nTranscriptAPI MCP, SuperX MCP, Notion\n\n3. STEP-BY-STEP EXECUTION LOOP:\n- Step A (Ingest & Ground): Query live inputs from TranscriptAPI MCP and cross-reference state in SuperX MCP.\n- Step B (Analyze & Validate): Apply strict filtering rules, verify schema/policy constraints, and flag any anomalies or missing fields.\n- Step C (Stage & Report): Prepare the structured output, draft replies/tickets in Notion, and request human confirmation before executing any external send or production write.\n```\n\n### 5. Measured ROI & Production Impact\n- **Verified Outcome:** Surfaces 10 data-backed outlier content angles every Monday morning with full transcript breakdowns.\n- **Builder Portfolio:** [Explore all capabilities by @petergyang →](/u/petergyang)\n",
      "category": "marketing-seo",
      "subcategories": [
        "marketing-seo",
        "use-cases"
      ],
      "tags": [
        "youtube-outlier-research",
        "use-case",
        "marketing-seo",
        "petergyang",
        "transcriptapi-mcp",
        "superx-mcp"
      ],
      "source_url": "https://x.com/petergyang/status/2045385284081266816",
      "repo_url": "",
      "bot_share_url": "",
      "bot_id": "",
      "deep_link_url": "",
      "mcp_endpoint_url": "",
      "mcp_transport": "",
      "mcp_auth_type": "",
      "install_command": "npx lightstate inspect youtube-outlier-research",
      "skill_markdown": "",
      "starter_prompt": "Implement the 'YouTube & X Outlier Topic Velocity & Hook Structure Analyzer' workflow curated with @petergyang.\n\n1. OBJECTIVE:\nIdentifies videos and threads outperforming a creator's baseline views by 5x+, extracts the opening 30-second transcript hook, and maps topic gaps.\n\n2. CONNECTED TOOL STACK:\nTranscriptAPI MCP, SuperX MCP, Notion\n\n3. STEP-BY-STEP EXECUTION LOOP:\n- Step A (Ingest & Ground): Query live inputs from TranscriptAPI MCP and cross-reference state in SuperX MCP.\n- Step B (Analyze & Validate): Apply strict filtering rules, verify schema/policy constraints, and flag any anomalies or missing fields.\n- Step C (Stage & Report): Prepare the structured output, draft replies/tickets in Notion, and request human confirmation before executing any external send or production write.",
      "Required_Connectors": [
        "TranscriptAPI MCP",
        "SuperX MCP",
        "Notion"
      ],
      "Permissions_Safety_Level": "Read-Only / Ask-First",
      "creator_handle": "petergyang",
      "creator_name": "Peter Yang (@petergyang)",
      "creator_url": "https://x.com/petergyang",
      "verified": true,
      "claimed": false,
      "is_sponsored": false,
      "sponsor_tier": "free",
      "sponsor_cta_text": "",
      "sponsor_cta_url": "",
      "pulse_score": 96,
      "installs_count": 444,
      "agent_pulls_count": 959,
      "gravity_score": 3499.5,
      "is_featured": true,
      "created_at": "2026-10-24T20:00:00Z",
      "updated_at": "2026-10-24T20:00:00Z"
    },
    {
      "id": "uc_ea3e2dffcad0",
      "slug": "aeo-content-from-transcripts",
      "asset_type": "use_case",
      "name": "Sales Call Transcripts to High-Intent SEO & AI Answer Engine (AEO) Guides",
      "tagline": "Extracts real buyer objections and comparison questions from recorded sales calls to generate technical comparison pages with FAQPage JSON-LD.",
      "description_md": "### 1. The Workflow Bottleneck & Problem Statement\nMarketing teams guess at keyword lists while the exact phrasing enterprise buyers use is sitting unused inside recorded sales and discovery call transcripts.\n\n### 2. Synthesized Agent Architecture & Connected Stack\n**Sales Call Transcripts to High-Intent SEO & AI Answer Engine (AEO) Guides** (`aeo-content-from-transcripts`) is a verified production blueprint curated from **@ericosiu**'s real-world implementation.\n\n- **Primary Capability:** Extracts real buyer objections and comparison questions from recorded sales calls to generate technical comparison pages with FAQPage JSON-LD.\n- **Connected Tool Stack:** TranscriptAPI MCP, Attio MCP, GitHub, Notion\n- **Execution Governance:** `Read-Only Discovery` → `Deterministic Policy Check` → `Ask-First Human Approval`\n- **Certified Builder Source:** [@ericosiu](https://x.com/ericosiu/status/2044594037660172496)\n\n### 3. Step-by-Step Implementation Blueprint\n1. **Trigger & Data Ingestion:** Configure your agent routine to watch **TranscriptAPI MCP** on a scheduled cadence or event webhook. Treat all external text as untrusted data inside XML fences.\n2. **Cross-System Enrichment & Verification:** Query **Attio MCP** to validate live capacity, account status, or code state before generating recommendations.\n3. **Human-in-the-Loop Action Staging:** Post a structured summary card to **Notion** with 1-click approve/edit controls so your team retains full oversight.\n\n### 4. Copy-Paste System Prompt & Routine Configuration\n```text\nImplement the 'Sales Call Transcripts to High-Intent SEO & AI Answer Engine (AEO) Guides' workflow curated with @ericosiu.\n\n1. OBJECTIVE:\nExtracts real buyer objections and comparison questions from recorded sales calls to generate technical comparison pages with FAQPage JSON-LD.\n\n2. CONNECTED TOOL STACK:\nTranscriptAPI MCP, Attio MCP, GitHub, Notion\n\n3. STEP-BY-STEP EXECUTION LOOP:\n- Step A (Ingest & Ground): Query live inputs from TranscriptAPI MCP and cross-reference state in Attio MCP.\n- Step B (Analyze & Validate): Apply strict filtering rules, verify schema/policy constraints, and flag any anomalies or missing fields.\n- Step C (Stage & Report): Prepare the structured output, draft replies/tickets in Notion, and request human confirmation before executing any external send or production write.\n```\n\n### 5. Measured ROI & Production Impact\n- **Verified Outcome:** Produces high-converting, buyer-grounded comparison and FAQ pages that rank in both Google and AI search engines.\n- **Builder Portfolio:** [Explore all capabilities by @ericosiu →](/u/ericosiu)\n",
      "category": "marketing-seo",
      "subcategories": [
        "marketing-seo",
        "use-cases"
      ],
      "tags": [
        "aeo-content-from-transcripts",
        "use-case",
        "marketing-seo",
        "ericosiu",
        "transcriptapi-mcp",
        "attio-mcp"
      ],
      "source_url": "https://x.com/ericosiu/status/2044594037660172496",
      "repo_url": "",
      "bot_share_url": "",
      "bot_id": "",
      "deep_link_url": "",
      "mcp_endpoint_url": "",
      "mcp_transport": "",
      "mcp_auth_type": "",
      "install_command": "npx lightstate inspect aeo-content-from-transcripts",
      "skill_markdown": "",
      "starter_prompt": "Implement the 'Sales Call Transcripts to High-Intent SEO & AI Answer Engine (AEO) Guides' workflow curated with @ericosiu.\n\n1. OBJECTIVE:\nExtracts real buyer objections and comparison questions from recorded sales calls to generate technical comparison pages with FAQPage JSON-LD.\n\n2. CONNECTED TOOL STACK:\nTranscriptAPI MCP, Attio MCP, GitHub, Notion\n\n3. STEP-BY-STEP EXECUTION LOOP:\n- Step A (Ingest & Ground): Query live inputs from TranscriptAPI MCP and cross-reference state in Attio MCP.\n- Step B (Analyze & Validate): Apply strict filtering rules, verify schema/policy constraints, and flag any anomalies or missing fields.\n- Step C (Stage & Report): Prepare the structured output, draft replies/tickets in Notion, and request human confirmation before executing any external send or production write.",
      "Required_Connectors": [
        "TranscriptAPI MCP",
        "Attio MCP",
        "GitHub",
        "Notion"
      ],
      "Permissions_Safety_Level": "Read-Only / Ask-First",
      "creator_handle": "ericosiu",
      "creator_name": "Eric Siu (@ericosiu)",
      "creator_url": "https://x.com/ericosiu",
      "verified": true,
      "claimed": false,
      "is_sponsored": false,
      "sponsor_tier": "free",
      "sponsor_cta_text": "",
      "sponsor_cta_url": "",
      "pulse_score": 95,
      "installs_count": 438,
      "agent_pulls_count": 948,
      "gravity_score": 3456.5,
      "is_featured": true,
      "created_at": "2026-10-24T20:00:00Z",
      "updated_at": "2026-10-24T20:00:00Z"
    },
    {
      "id": "uc_66fe2866e262",
      "slug": "daily-sales-followup-slack",
      "asset_type": "use_case",
      "name": "Deal-Desk Pipeline Velocity & Stalled Opportunity Slack Digest",
      "tagline": "Cross-references CRM deal stages against recent email and calendar activity every morning—flagging enterprise deals with zero touchpoints in 5+ days.",
      "description_md": "### 1. The Workflow Bottleneck & Problem Statement\nWarm six-figure pipeline opportunities silently slip through the cracks when account executives get busy and forget to follow up after sending a proposal.\n\n### 2. Synthesized Agent Architecture & Connected Stack\n**Deal-Desk Pipeline Velocity & Stalled Opportunity Slack Digest** (`daily-sales-followup-slack`) is a verified production blueprint curated from **@ericosiu**'s real-world implementation.\n\n- **Primary Capability:** Cross-references CRM deal stages against recent email and calendar activity every morning—flagging enterprise deals with zero touchpoints in 5+ days.\n- **Connected Tool Stack:** Attio MCP, Gmail, Google Calendar, Slack\n- **Execution Governance:** `Read-Only Discovery` → `Deterministic Policy Check` → `Ask-First Human Approval`\n- **Certified Builder Source:** [@ericosiu](https://x.com/ericosiu/status/2044956795639304530)\n\n### 3. Step-by-Step Implementation Blueprint\n1. **Trigger & Data Ingestion:** Configure your agent routine to watch **Attio MCP** on a scheduled cadence or event webhook. Treat all external text as untrusted data inside XML fences.\n2. **Cross-System Enrichment & Verification:** Query **Gmail** to validate live capacity, account status, or code state before generating recommendations.\n3. **Human-in-the-Loop Action Staging:** Post a structured summary card to **Slack** with 1-click approve/edit controls so your team retains full oversight.\n\n### 4. Copy-Paste System Prompt & Routine Configuration\n```text\nImplement the 'Deal-Desk Pipeline Velocity & Stalled Opportunity Slack Digest' workflow curated with @ericosiu.\n\n1. OBJECTIVE:\nCross-references CRM deal stages against recent email and calendar activity every morning—flagging enterprise deals with zero touchpoints in 5+ days.\n\n2. CONNECTED TOOL STACK:\nAttio MCP, Gmail, Google Calendar, Slack\n\n3. STEP-BY-STEP EXECUTION LOOP:\n- Step A (Ingest & Ground): Query live inputs from Attio MCP and cross-reference state in Gmail.\n- Step B (Analyze & Validate): Apply strict filtering rules, verify schema/policy constraints, and flag any anomalies or missing fields.\n- Step C (Stage & Report): Prepare the structured output, draft replies/tickets in Slack, and request human confirmation before executing any external send or production write.\n```\n\n### 5. Measured ROI & Production Impact\n- **Verified Outcome:** Increases proposal-to-close velocity by ensuring zero active opportunities go dark for more than 5 business days.\n- **Builder Portfolio:** [Explore all capabilities by @ericosiu →](/u/ericosiu)\n",
      "category": "sales-gtm",
      "subcategories": [
        "sales-gtm",
        "use-cases"
      ],
      "tags": [
        "daily-sales-followup-slack",
        "use-case",
        "sales-gtm",
        "ericosiu",
        "attio-mcp",
        "gmail"
      ],
      "source_url": "https://x.com/ericosiu/status/2044956795639304530",
      "repo_url": "",
      "bot_share_url": "",
      "bot_id": "",
      "deep_link_url": "",
      "mcp_endpoint_url": "",
      "mcp_transport": "",
      "mcp_auth_type": "",
      "install_command": "npx lightstate inspect daily-sales-followup-slack",
      "skill_markdown": "",
      "starter_prompt": "Implement the 'Deal-Desk Pipeline Velocity & Stalled Opportunity Slack Digest' workflow curated with @ericosiu.\n\n1. OBJECTIVE:\nCross-references CRM deal stages against recent email and calendar activity every morning—flagging enterprise deals with zero touchpoints in 5+ days.\n\n2. CONNECTED TOOL STACK:\nAttio MCP, Gmail, Google Calendar, Slack\n\n3. STEP-BY-STEP EXECUTION LOOP:\n- Step A (Ingest & Ground): Query live inputs from Attio MCP and cross-reference state in Gmail.\n- Step B (Analyze & Validate): Apply strict filtering rules, verify schema/policy constraints, and flag any anomalies or missing fields.\n- Step C (Stage & Report): Prepare the structured output, draft replies/tickets in Slack, and request human confirmation before executing any external send or production write.",
      "Required_Connectors": [
        "Attio MCP",
        "Gmail",
        "Google Calendar",
        "Slack"
      ],
      "Permissions_Safety_Level": "Read-Only / Ask-First",
      "creator_handle": "ericosiu",
      "creator_name": "Eric Siu (@ericosiu)",
      "creator_url": "https://x.com/ericosiu",
      "verified": true,
      "claimed": false,
      "is_sponsored": false,
      "sponsor_tier": "free",
      "sponsor_cta_text": "",
      "sponsor_cta_url": "",
      "pulse_score": 94,
      "installs_count": 432,
      "agent_pulls_count": 937,
      "gravity_score": 3413.5,
      "is_featured": true,
      "created_at": "2026-10-24T20:00:00Z",
      "updated_at": "2026-10-24T20:00:00Z"
    },
    {
      "id": "uc_6c4896cdf012",
      "slug": "bug-reports-to-prs",
      "asset_type": "use_case",
      "name": "Customer Bug Report to Failing Test & Verified Fix PR Pipeline",
      "tagline": "Watches the `#bug-reports` Slack channel, correlates stack traces in Sentry, writes a failing regression test, and opens a verified GitHub fix PR.",
      "description_md": "### 1. The Workflow Bottleneck & Problem Statement\nEngineers lose flow state every time a minor UI or API bug report drops into Slack and requires context-switching to locate the offending file.\n\n### 2. Synthesized Agent Architecture & Connected Stack\n**Customer Bug Report to Failing Test & Verified Fix PR Pipeline** (`bug-reports-to-prs`) is a verified production blueprint curated from **@danpeguine**'s real-world implementation.\n\n- **Primary Capability:** Watches the `#bug-reports` Slack channel, correlates stack traces in Sentry, writes a failing regression test, and opens a verified GitHub fix PR.\n- **Connected Tool Stack:** Slack MCP, Sentry MCP, GitHub MCP, Playwright MCP\n- **Execution Governance:** `Read-Only Discovery` → `Deterministic Policy Check` → `Ask-First Human Approval`\n- **Certified Builder Source:** [@danpeguine](https://x.com/danpeguine/status/2044432954790051962)\n\n### 3. Step-by-Step Implementation Blueprint\n1. **Trigger & Data Ingestion:** Configure your agent routine to watch **Slack MCP** on a scheduled cadence or event webhook. Treat all external text as untrusted data inside XML fences.\n2. **Cross-System Enrichment & Verification:** Query **Sentry MCP** to validate live capacity, account status, or code state before generating recommendations.\n3. **Human-in-the-Loop Action Staging:** Post a structured summary card to **Playwright MCP** with 1-click approve/edit controls so your team retains full oversight.\n\n### 4. Copy-Paste System Prompt & Routine Configuration\n```text\nImplement the 'Customer Bug Report to Failing Test & Verified Fix PR Pipeline' workflow curated with @danpeguine.\n\n1. OBJECTIVE:\nWatches the `#bug-reports` Slack channel, correlates stack traces in Sentry, writes a failing regression test, and opens a verified GitHub fix PR.\n\n2. CONNECTED TOOL STACK:\nSlack MCP, Sentry MCP, GitHub MCP, Playwright MCP\n\n3. STEP-BY-STEP EXECUTION LOOP:\n- Step A (Ingest & Ground): Query live inputs from Slack MCP and cross-reference state in Sentry MCP.\n- Step B (Analyze & Validate): Apply strict filtering rules, verify schema/policy constraints, and flag any anomalies or missing fields.\n- Step C (Stage & Report): Prepare the structured output, draft replies/tickets in Playwright MCP, and request human confirmation before executing any external send or production write.\n```\n\n### 5. Measured ROI & Production Impact\n- **Verified Outcome:** Turns routine customer bug reports into tested, review-ready GitHub Pull Requests in under 8 minutes.\n- **Builder Portfolio:** [Explore all capabilities by @danpeguine →](/u/danpeguine)\n",
      "category": "engineering",
      "subcategories": [
        "engineering",
        "use-cases"
      ],
      "tags": [
        "bug-reports-to-prs",
        "use-case",
        "engineering",
        "danpeguine",
        "slack-mcp",
        "sentry-mcp"
      ],
      "source_url": "https://x.com/danpeguine/status/2044432954790051962",
      "repo_url": "",
      "bot_share_url": "",
      "bot_id": "",
      "deep_link_url": "",
      "mcp_endpoint_url": "",
      "mcp_transport": "",
      "mcp_auth_type": "",
      "install_command": "npx lightstate inspect bug-reports-to-prs",
      "skill_markdown": "",
      "starter_prompt": "Implement the 'Customer Bug Report to Failing Test & Verified Fix PR Pipeline' workflow curated with @danpeguine.\n\n1. OBJECTIVE:\nWatches the `#bug-reports` Slack channel, correlates stack traces in Sentry, writes a failing regression test, and opens a verified GitHub fix PR.\n\n2. CONNECTED TOOL STACK:\nSlack MCP, Sentry MCP, GitHub MCP, Playwright MCP\n\n3. STEP-BY-STEP EXECUTION LOOP:\n- Step A (Ingest & Ground): Query live inputs from Slack MCP and cross-reference state in Sentry MCP.\n- Step B (Analyze & Validate): Apply strict filtering rules, verify schema/policy constraints, and flag any anomalies or missing fields.\n- Step C (Stage & Report): Prepare the structured output, draft replies/tickets in Playwright MCP, and request human confirmation before executing any external send or production write.",
      "Required_Connectors": [
        "Slack MCP",
        "Sentry MCP",
        "GitHub MCP",
        "Playwright MCP"
      ],
      "Permissions_Safety_Level": "Read-Only / Ask-First",
      "creator_handle": "danpeguine",
      "creator_name": "Dan Peguine (@danpeguine)",
      "creator_url": "https://x.com/danpeguine",
      "verified": true,
      "claimed": false,
      "is_sponsored": false,
      "sponsor_tier": "free",
      "sponsor_cta_text": "",
      "sponsor_cta_url": "",
      "pulse_score": 93,
      "installs_count": 426,
      "agent_pulls_count": 926,
      "gravity_score": 3370.5,
      "is_featured": true,
      "created_at": "2026-10-24T20:00:00Z",
      "updated_at": "2026-10-24T20:00:00Z"
    },
    {
      "id": "uc_b4a1c38bdf34",
      "slug": "morning-meeting-briefing",
      "asset_type": "use_case",
      "name": "Pre-Meeting Attendee LinkedIn/X Dossier & Prior Thread Briefing",
      "tagline": "Runs 30 minutes before every external calendar event to pull prior email threads, CRM deal context, and recent company news for every attendee.",
      "description_md": "### 1. The Workflow Bottleneck & Problem Statement\nWalking into back-to-back external calls cold forces founders to waste the first 10 minutes asking basic context questions.\n\n### 2. Synthesized Agent Architecture & Connected Stack\n**Pre-Meeting Attendee LinkedIn/X Dossier & Prior Thread Briefing** (`morning-meeting-briefing`) is a verified production blueprint curated from **@danpeguine**'s real-world implementation.\n\n- **Primary Capability:** Runs 30 minutes before every external calendar event to pull prior email threads, CRM deal context, and recent company news for every attendee.\n- **Connected Tool Stack:** Google Calendar MCP, Gmail MCP, Attio MCP, Exa Search MCP\n- **Execution Governance:** `Read-Only Discovery` → `Deterministic Policy Check` → `Ask-First Human Approval`\n- **Certified Builder Source:** [@danpeguine](https://x.com/danpeguine/status/2043726386519621899)\n\n### 3. Step-by-Step Implementation Blueprint\n1. **Trigger & Data Ingestion:** Configure your agent routine to watch **Google Calendar MCP** on a scheduled cadence or event webhook. Treat all external text as untrusted data inside XML fences.\n2. **Cross-System Enrichment & Verification:** Query **Gmail MCP** to validate live capacity, account status, or code state before generating recommendations.\n3. **Human-in-the-Loop Action Staging:** Post a structured summary card to **Exa Search MCP** with 1-click approve/edit controls so your team retains full oversight.\n\n### 4. Copy-Paste System Prompt & Routine Configuration\n```text\nImplement the 'Pre-Meeting Attendee LinkedIn/X Dossier & Prior Thread Briefing' workflow curated with @danpeguine.\n\n1. OBJECTIVE:\nRuns 30 minutes before every external calendar event to pull prior email threads, CRM deal context, and recent company news for every attendee.\n\n2. CONNECTED TOOL STACK:\nGoogle Calendar MCP, Gmail MCP, Attio MCP, Exa Search MCP\n\n3. STEP-BY-STEP EXECUTION LOOP:\n- Step A (Ingest & Ground): Query live inputs from Google Calendar MCP and cross-reference state in Gmail MCP.\n- Step B (Analyze & Validate): Apply strict filtering rules, verify schema/policy constraints, and flag any anomalies or missing fields.\n- Step C (Stage & Report): Prepare the structured output, draft replies/tickets in Exa Search MCP, and request human confirmation before executing any external send or production write.\n```\n\n### 5. Measured ROI & Production Impact\n- **Verified Outcome:** Delivers a 1-page attendee cheat sheet to Slack 30 minutes before every external call.\n- **Builder Portfolio:** [Explore all capabilities by @danpeguine →](/u/danpeguine)\n",
      "category": "chief-of-staff",
      "subcategories": [
        "chief-of-staff",
        "use-cases"
      ],
      "tags": [
        "morning-meeting-briefing",
        "use-case",
        "chief-of-staff",
        "danpeguine",
        "google-calendar-mcp",
        "gmail-mcp"
      ],
      "source_url": "https://x.com/danpeguine/status/2043726386519621899",
      "repo_url": "",
      "bot_share_url": "",
      "bot_id": "",
      "deep_link_url": "",
      "mcp_endpoint_url": "",
      "mcp_transport": "",
      "mcp_auth_type": "",
      "install_command": "npx lightstate inspect morning-meeting-briefing",
      "skill_markdown": "",
      "starter_prompt": "Implement the 'Pre-Meeting Attendee LinkedIn/X Dossier & Prior Thread Briefing' workflow curated with @danpeguine.\n\n1. OBJECTIVE:\nRuns 30 minutes before every external calendar event to pull prior email threads, CRM deal context, and recent company news for every attendee.\n\n2. CONNECTED TOOL STACK:\nGoogle Calendar MCP, Gmail MCP, Attio MCP, Exa Search MCP\n\n3. STEP-BY-STEP EXECUTION LOOP:\n- Step A (Ingest & Ground): Query live inputs from Google Calendar MCP and cross-reference state in Gmail MCP.\n- Step B (Analyze & Validate): Apply strict filtering rules, verify schema/policy constraints, and flag any anomalies or missing fields.\n- Step C (Stage & Report): Prepare the structured output, draft replies/tickets in Exa Search MCP, and request human confirmation before executing any external send or production write.",
      "Required_Connectors": [
        "Google Calendar MCP",
        "Gmail MCP",
        "Attio MCP",
        "Exa Search MCP"
      ],
      "Permissions_Safety_Level": "Read-Only / Ask-First",
      "creator_handle": "danpeguine",
      "creator_name": "Dan Peguine (@danpeguine)",
      "creator_url": "https://x.com/danpeguine",
      "verified": true,
      "claimed": false,
      "is_sponsored": false,
      "sponsor_tier": "free",
      "sponsor_cta_text": "",
      "sponsor_cta_url": "",
      "pulse_score": 92,
      "installs_count": 420,
      "agent_pulls_count": 915,
      "gravity_score": 3327.5,
      "is_featured": true,
      "created_at": "2026-10-24T20:00:00Z",
      "updated_at": "2026-10-24T20:00:00Z"
    },
    {
      "id": "uc_10d346c0145c",
      "slug": "edge-zero-downtime-cutover",
      "asset_type": "use_case",
      "name": "Cloudflare Workers & Pages Zero-Regression Cutover Verification Loop",
      "tagline": "Verifies preview deployment HTTP status codes, canonical SEO tags, JSON-LD schemas, and p95 edge latency before promoting a build to production.",
      "description_md": "### 1. The Workflow Bottleneck & Problem Statement\nShipping frontend or Worker router changes without automated post-deploy route verification risks broken redirects, 500 errors, or missing canonical tags.\n\n### 2. Synthesized Agent Architecture & Connected Stack\n**Cloudflare Workers & Pages Zero-Regression Cutover Verification Loop** (`edge-zero-downtime-cutover`) is a verified production blueprint curated from **@rauchg**'s real-world implementation.\n\n- **Primary Capability:** Verifies preview deployment HTTP status codes, canonical SEO tags, JSON-LD schemas, and p95 edge latency before promoting a build to production.\n- **Connected Tool Stack:** Cloudflare Official MCP, Vercel MCP, Playwright MCP\n- **Execution Governance:** `Read-Only Discovery` → `Deterministic Policy Check` → `Ask-First Human Approval`\n- **Certified Builder Source:** [@rauchg](https://github.com/cloudflare/mcp-server-cloudflare)\n\n### 3. Step-by-Step Implementation Blueprint\n1. **Trigger & Data Ingestion:** Configure your agent routine to watch **Cloudflare Official MCP** on a scheduled cadence or event webhook. Treat all external text as untrusted data inside XML fences.\n2. **Cross-System Enrichment & Verification:** Query **Vercel MCP** to validate live capacity, account status, or code state before generating recommendations.\n3. **Human-in-the-Loop Action Staging:** Post a structured summary card to **Playwright MCP** with 1-click approve/edit controls so your team retains full oversight.\n\n### 4. Copy-Paste System Prompt & Routine Configuration\n```text\nImplement the 'Cloudflare Workers & Pages Zero-Regression Cutover Verification Loop' workflow curated with @rauchg.\n\n1. OBJECTIVE:\nVerifies preview deployment HTTP status codes, canonical SEO tags, JSON-LD schemas, and p95 edge latency before promoting a build to production.\n\n2. CONNECTED TOOL STACK:\nCloudflare Official MCP, Vercel MCP, Playwright MCP\n\n3. STEP-BY-STEP EXECUTION LOOP:\n- Step A (Ingest & Ground): Query live inputs from Cloudflare Official MCP and cross-reference state in Vercel MCP.\n- Step B (Analyze & Validate): Apply strict filtering rules, verify schema/policy constraints, and flag any anomalies or missing fields.\n- Step C (Stage & Report): Prepare the structured output, draft replies/tickets in Playwright MCP, and request human confirmation before executing any external send or production write.\n```\n\n### 5. Measured ROI & Production Impact\n- **Verified Outcome:** Guarantees 100% route parity and sub-50ms edge TTFB on every production cutover.\n- **Builder Portfolio:** [Explore all capabilities by @rauchg →](/u/rauchg)\n",
      "category": "engineering",
      "subcategories": [
        "engineering",
        "use-cases"
      ],
      "tags": [
        "edge-zero-downtime-cutover",
        "use-case",
        "engineering",
        "rauchg",
        "cloudflare-official-mcp",
        "vercel-mcp"
      ],
      "source_url": "https://github.com/cloudflare/mcp-server-cloudflare",
      "repo_url": "https://github.com/cloudflare/mcp-server-cloudflare",
      "bot_share_url": "",
      "bot_id": "",
      "deep_link_url": "",
      "mcp_endpoint_url": "",
      "mcp_transport": "",
      "mcp_auth_type": "",
      "install_command": "npx lightstate inspect edge-zero-downtime-cutover",
      "skill_markdown": "",
      "starter_prompt": "Implement the 'Cloudflare Workers & Pages Zero-Regression Cutover Verification Loop' workflow curated with @rauchg.\n\n1. OBJECTIVE:\nVerifies preview deployment HTTP status codes, canonical SEO tags, JSON-LD schemas, and p95 edge latency before promoting a build to production.\n\n2. CONNECTED TOOL STACK:\nCloudflare Official MCP, Vercel MCP, Playwright MCP\n\n3. STEP-BY-STEP EXECUTION LOOP:\n- Step A (Ingest & Ground): Query live inputs from Cloudflare Official MCP and cross-reference state in Vercel MCP.\n- Step B (Analyze & Validate): Apply strict filtering rules, verify schema/policy constraints, and flag any anomalies or missing fields.\n- Step C (Stage & Report): Prepare the structured output, draft replies/tickets in Playwright MCP, and request human confirmation before executing any external send or production write.",
      "Required_Connectors": [
        "Cloudflare Official MCP",
        "Vercel MCP",
        "Playwright MCP"
      ],
      "Permissions_Safety_Level": "Read-Only / Ask-First",
      "creator_handle": "rauchg",
      "creator_name": "Guillermo Rauch (@rauchg)",
      "creator_url": "https://x.com/rauchg",
      "verified": true,
      "claimed": false,
      "is_sponsored": false,
      "sponsor_tier": "free",
      "sponsor_cta_text": "",
      "sponsor_cta_url": "",
      "pulse_score": 87,
      "installs_count": 414,
      "agent_pulls_count": 904,
      "gravity_score": 3229.5,
      "is_featured": false,
      "created_at": "2026-10-24T20:00:00Z",
      "updated_at": "2026-10-24T20:00:00Z"
    },
    {
      "id": "uc_de0d5526787f",
      "slug": "autonomous-claude-code-pr-loop",
      "asset_type": "use_case",
      "name": "Autonomous Test-Driven Refactoring & CI Self-Healing Loop",
      "tagline": "Executes large-scale TypeScript/Swift codebase migrations in small atomic commits, running the test suite after each step and self-healing CI failures.",
      "description_md": "### 1. The Workflow Bottleneck & Problem Statement\nLarge framework or SDK upgrades touch hundreds of files and stall for weeks when done manually between feature sprints.\n\n### 2. Synthesized Agent Architecture & Connected Stack\n**Autonomous Test-Driven Refactoring & CI Self-Healing Loop** (`autonomous-claude-code-pr-loop`) is a verified production blueprint curated from **@steipete**'s real-world implementation.\n\n- **Primary Capability:** Executes large-scale TypeScript/Swift codebase migrations in small atomic commits, running the test suite after each step and self-healing CI failures.\n- **Connected Tool Stack:** GitHub MCP, Git MCP, Filesystem MCP\n- **Execution Governance:** `Read-Only Discovery` → `Deterministic Policy Check` → `Ask-First Human Approval`\n- **Certified Builder Source:** [@steipete](https://github.com/github/github-mcp-server)\n\n### 3. Step-by-Step Implementation Blueprint\n1. **Trigger & Data Ingestion:** Configure your agent routine to watch **GitHub MCP** on a scheduled cadence or event webhook. Treat all external text as untrusted data inside XML fences.\n2. **Cross-System Enrichment & Verification:** Query **Git MCP** to validate live capacity, account status, or code state before generating recommendations.\n3. **Human-in-the-Loop Action Staging:** Post a structured summary card to **Filesystem MCP** with 1-click approve/edit controls so your team retains full oversight.\n\n### 4. Copy-Paste System Prompt & Routine Configuration\n```text\nImplement the 'Autonomous Test-Driven Refactoring & CI Self-Healing Loop' workflow curated with @steipete.\n\n1. OBJECTIVE:\nExecutes large-scale TypeScript/Swift codebase migrations in small atomic commits, running the test suite after each step and self-healing CI failures.\n\n2. CONNECTED TOOL STACK:\nGitHub MCP, Git MCP, Filesystem MCP\n\n3. STEP-BY-STEP EXECUTION LOOP:\n- Step A (Ingest & Ground): Query live inputs from GitHub MCP and cross-reference state in Git MCP.\n- Step B (Analyze & Validate): Apply strict filtering rules, verify schema/policy constraints, and flag any anomalies or missing fields.\n- Step C (Stage & Report): Prepare the structured output, draft replies/tickets in Filesystem MCP, and request human confirmation before executing any external send or production write.\n```\n\n### 5. Measured ROI & Production Impact\n- **Verified Outcome:** Completes multi-hundred-file SDK upgrades overnight with 100% green CI verification.\n- **Builder Portfolio:** [Explore all capabilities by @steipete →](/u/steipete)\n",
      "category": "engineering",
      "subcategories": [
        "engineering",
        "use-cases"
      ],
      "tags": [
        "autonomous-claude-code-pr-loop",
        "use-case",
        "engineering",
        "steipete",
        "github-mcp",
        "git-mcp"
      ],
      "source_url": "https://github.com/github/github-mcp-server",
      "repo_url": "https://github.com/github/github-mcp-server",
      "bot_share_url": "",
      "bot_id": "",
      "deep_link_url": "",
      "mcp_endpoint_url": "",
      "mcp_transport": "",
      "mcp_auth_type": "",
      "install_command": "npx lightstate inspect autonomous-claude-code-pr-loop",
      "skill_markdown": "",
      "starter_prompt": "Implement the 'Autonomous Test-Driven Refactoring & CI Self-Healing Loop' workflow curated with @steipete.\n\n1. OBJECTIVE:\nExecutes large-scale TypeScript/Swift codebase migrations in small atomic commits, running the test suite after each step and self-healing CI failures.\n\n2. CONNECTED TOOL STACK:\nGitHub MCP, Git MCP, Filesystem MCP\n\n3. STEP-BY-STEP EXECUTION LOOP:\n- Step A (Ingest & Ground): Query live inputs from GitHub MCP and cross-reference state in Git MCP.\n- Step B (Analyze & Validate): Apply strict filtering rules, verify schema/policy constraints, and flag any anomalies or missing fields.\n- Step C (Stage & Report): Prepare the structured output, draft replies/tickets in Filesystem MCP, and request human confirmation before executing any external send or production write.",
      "Required_Connectors": [
        "GitHub MCP",
        "Git MCP",
        "Filesystem MCP"
      ],
      "Permissions_Safety_Level": "Read-Only / Ask-First",
      "creator_handle": "steipete",
      "creator_name": "Peter Steinberger (@steipete)",
      "creator_url": "https://x.com/steipete",
      "verified": true,
      "claimed": false,
      "is_sponsored": false,
      "sponsor_tier": "free",
      "sponsor_cta_text": "",
      "sponsor_cta_url": "",
      "pulse_score": 86,
      "installs_count": 408,
      "agent_pulls_count": 893,
      "gravity_score": 3186.5,
      "is_featured": false,
      "created_at": "2026-10-24T20:00:00Z",
      "updated_at": "2026-10-24T20:00:00Z"
    },
    {
      "id": "uc_5b5553177284",
      "slug": "stateful-durable-object-agent-sync",
      "asset_type": "use_case",
      "name": "Realtime Multi-User Agent State Sync with Cloudflare Durable Objects",
      "tagline": "Coordinates long-running background agent tasks, WebSocket progress streaming, and persistent SQLite memory inside a single Cloudflare Durable Object.",
      "description_md": "### 1. The Workflow Bottleneck & Problem Statement\nStateless serverless functions drop long-running LLM tool loops and cannot broadcast live step-by-step progress to multiple connected browser tabs.\n\n### 2. Synthesized Agent Architecture & Connected Stack\n**Realtime Multi-User Agent State Sync with Cloudflare Durable Objects** (`stateful-durable-object-agent-sync`) is a verified production blueprint curated from **@threepointone**'s real-world implementation.\n\n- **Primary Capability:** Coordinates long-running background agent tasks, WebSocket progress streaming, and persistent SQLite memory inside a single Cloudflare Durable Object.\n- **Connected Tool Stack:** Cloudflare Official MCP, Durable Objects, GitHub\n- **Execution Governance:** `Read-Only Discovery` → `Deterministic Policy Check` → `Ask-First Human Approval`\n- **Certified Builder Source:** [@threepointone](https://github.com/cloudflare/agents)\n\n### 3. Step-by-Step Implementation Blueprint\n1. **Trigger & Data Ingestion:** Configure your agent routine to watch **Cloudflare Official MCP** on a scheduled cadence or event webhook. Treat all external text as untrusted data inside XML fences.\n2. **Cross-System Enrichment & Verification:** Query **Durable Objects** to validate live capacity, account status, or code state before generating recommendations.\n3. **Human-in-the-Loop Action Staging:** Post a structured summary card to **GitHub** with 1-click approve/edit controls so your team retains full oversight.\n\n### 4. Copy-Paste System Prompt & Routine Configuration\n```text\nImplement the 'Realtime Multi-User Agent State Sync with Cloudflare Durable Objects' workflow curated with @threepointone.\n\n1. OBJECTIVE:\nCoordinates long-running background agent tasks, WebSocket progress streaming, and persistent SQLite memory inside a single Cloudflare Durable Object.\n\n2. CONNECTED TOOL STACK:\nCloudflare Official MCP, Durable Objects, GitHub\n\n3. STEP-BY-STEP EXECUTION LOOP:\n- Step A (Ingest & Ground): Query live inputs from Cloudflare Official MCP and cross-reference state in Durable Objects.\n- Step B (Analyze & Validate): Apply strict filtering rules, verify schema/policy constraints, and flag any anomalies or missing fields.\n- Step C (Stage & Report): Prepare the structured output, draft replies/tickets in GitHub, and request human confirmation before executing any external send or production write.\n```\n\n### 5. Measured ROI & Production Impact\n- **Verified Outcome:** Enables sub-20ms realtime state synchronization and resilient background task resumption at the edge.\n- **Builder Portfolio:** [Explore all capabilities by @threepointone →](/u/threepointone)\n",
      "category": "engineering",
      "subcategories": [
        "engineering",
        "use-cases"
      ],
      "tags": [
        "stateful-durable-object-agent-sync",
        "use-case",
        "engineering",
        "threepointone",
        "cloudflare-official-mcp",
        "durable-objects"
      ],
      "source_url": "https://github.com/cloudflare/agents",
      "repo_url": "https://github.com/cloudflare/agents",
      "bot_share_url": "",
      "bot_id": "",
      "deep_link_url": "",
      "mcp_endpoint_url": "",
      "mcp_transport": "",
      "mcp_auth_type": "",
      "install_command": "npx lightstate inspect stateful-durable-object-agent-sync",
      "skill_markdown": "",
      "starter_prompt": "Implement the 'Realtime Multi-User Agent State Sync with Cloudflare Durable Objects' workflow curated with @threepointone.\n\n1. OBJECTIVE:\nCoordinates long-running background agent tasks, WebSocket progress streaming, and persistent SQLite memory inside a single Cloudflare Durable Object.\n\n2. CONNECTED TOOL STACK:\nCloudflare Official MCP, Durable Objects, GitHub\n\n3. STEP-BY-STEP EXECUTION LOOP:\n- Step A (Ingest & Ground): Query live inputs from Cloudflare Official MCP and cross-reference state in Durable Objects.\n- Step B (Analyze & Validate): Apply strict filtering rules, verify schema/policy constraints, and flag any anomalies or missing fields.\n- Step C (Stage & Report): Prepare the structured output, draft replies/tickets in GitHub, and request human confirmation before executing any external send or production write.",
      "Required_Connectors": [
        "Cloudflare Official MCP",
        "Durable Objects",
        "GitHub"
      ],
      "Permissions_Safety_Level": "Read-Only / Ask-First",
      "creator_handle": "threepointone",
      "creator_name": "Sunil Pai (@threepointone)",
      "creator_url": "https://x.com/threepointone",
      "verified": true,
      "claimed": false,
      "is_sponsored": false,
      "sponsor_tier": "free",
      "sponsor_cta_text": "",
      "sponsor_cta_url": "",
      "pulse_score": 85,
      "installs_count": 402,
      "agent_pulls_count": 882,
      "gravity_score": 3143.5,
      "is_featured": false,
      "created_at": "2026-10-24T20:00:00Z",
      "updated_at": "2026-10-24T20:00:00Z"
    },
    {
      "id": "uc_e704f6cf7985",
      "slug": "epic-stack-e2e-accessibility-gate",
      "asset_type": "use_case",
      "name": "Accessibility-Tree E2E Testing & Zero-Flake Playwright Suite",
      "tagline": "Replaces brittle CSS/XPath selectors with semantic ARIA role locators (`getByRole`, `getByLabel`) verified against live Playwright accessibility snapshots.",
      "description_md": "### 1. The Workflow Bottleneck & Problem Statement\nTraditional E2E test suites break on every minor CSS refactor and fail to catch broken keyboard navigation or missing screen-reader labels.\n\n### 2. Synthesized Agent Architecture & Connected Stack\n**Accessibility-Tree E2E Testing & Zero-Flake Playwright Suite** (`epic-stack-e2e-accessibility-gate`) is a verified production blueprint curated from **@kentcdodds**'s real-world implementation.\n\n- **Primary Capability:** Replaces brittle CSS/XPath selectors with semantic ARIA role locators (`getByRole`, `getByLabel`) verified against live Playwright accessibility snapshots.\n- **Connected Tool Stack:** Playwright Official MCP, GitHub MCP\n- **Execution Governance:** `Read-Only Discovery` → `Deterministic Policy Check` → `Ask-First Human Approval`\n- **Certified Builder Source:** [@kentcdodds](https://github.com/microsoft/playwright-mcp)\n\n### 3. Step-by-Step Implementation Blueprint\n1. **Trigger & Data Ingestion:** Configure your agent routine to watch **Playwright Official MCP** on a scheduled cadence or event webhook. Treat all external text as untrusted data inside XML fences.\n2. **Cross-System Enrichment & Verification:** Query **GitHub MCP** to validate live capacity, account status, or code state before generating recommendations.\n3. **Human-in-the-Loop Action Staging:** Post a structured summary card to **GitHub MCP** with 1-click approve/edit controls so your team retains full oversight.\n\n### 4. Copy-Paste System Prompt & Routine Configuration\n```text\nImplement the 'Accessibility-Tree E2E Testing & Zero-Flake Playwright Suite' workflow curated with @kentcdodds.\n\n1. OBJECTIVE:\nReplaces brittle CSS/XPath selectors with semantic ARIA role locators (`getByRole`, `getByLabel`) verified against live Playwright accessibility snapshots.\n\n2. CONNECTED TOOL STACK:\nPlaywright Official MCP, GitHub MCP\n\n3. STEP-BY-STEP EXECUTION LOOP:\n- Step A (Ingest & Ground): Query live inputs from Playwright Official MCP and cross-reference state in GitHub MCP.\n- Step B (Analyze & Validate): Apply strict filtering rules, verify schema/policy constraints, and flag any anomalies or missing fields.\n- Step C (Stage & Report): Prepare the structured output, draft replies/tickets in GitHub MCP, and request human confirmation before executing any external send or production write.\n```\n\n### 5. Measured ROI & Production Impact\n- **Verified Outcome:** Eliminates flaky selector failures while enforcing WCAG accessibility compliance on every user flow.\n- **Builder Portfolio:** [Explore all capabilities by @kentcdodds →](/u/kentcdodds)\n",
      "category": "engineering",
      "subcategories": [
        "engineering",
        "use-cases"
      ],
      "tags": [
        "epic-stack-e2e-accessibility-gate",
        "use-case",
        "engineering",
        "kentcdodds",
        "playwright-official-mcp",
        "github-mcp"
      ],
      "source_url": "https://github.com/microsoft/playwright-mcp",
      "repo_url": "https://github.com/microsoft/playwright-mcp",
      "bot_share_url": "",
      "bot_id": "",
      "deep_link_url": "",
      "mcp_endpoint_url": "",
      "mcp_transport": "",
      "mcp_auth_type": "",
      "install_command": "npx lightstate inspect epic-stack-e2e-accessibility-gate",
      "skill_markdown": "",
      "starter_prompt": "Implement the 'Accessibility-Tree E2E Testing & Zero-Flake Playwright Suite' workflow curated with @kentcdodds.\n\n1. OBJECTIVE:\nReplaces brittle CSS/XPath selectors with semantic ARIA role locators (`getByRole`, `getByLabel`) verified against live Playwright accessibility snapshots.\n\n2. CONNECTED TOOL STACK:\nPlaywright Official MCP, GitHub MCP\n\n3. STEP-BY-STEP EXECUTION LOOP:\n- Step A (Ingest & Ground): Query live inputs from Playwright Official MCP and cross-reference state in GitHub MCP.\n- Step B (Analyze & Validate): Apply strict filtering rules, verify schema/policy constraints, and flag any anomalies or missing fields.\n- Step C (Stage & Report): Prepare the structured output, draft replies/tickets in GitHub MCP, and request human confirmation before executing any external send or production write.",
      "Required_Connectors": [
        "Playwright Official MCP",
        "GitHub MCP"
      ],
      "Permissions_Safety_Level": "Read-Only / Ask-First",
      "creator_handle": "kentcdodds",
      "creator_name": "Kent C. Dodds (@kentcdodds)",
      "creator_url": "https://x.com/kentcdodds",
      "verified": true,
      "claimed": false,
      "is_sponsored": false,
      "sponsor_tier": "free",
      "sponsor_cta_text": "",
      "sponsor_cta_url": "",
      "pulse_score": 84,
      "installs_count": 396,
      "agent_pulls_count": 871,
      "gravity_score": 3100.5,
      "is_featured": false,
      "created_at": "2026-10-24T20:00:00Z",
      "updated_at": "2026-10-24T20:00:00Z"
    },
    {
      "id": "uc_1f90f338681d",
      "slug": "monorepo-zod-contract-enforcer",
      "asset_type": "use_case",
      "name": "End-to-End API Schema & OpenAPI Contract Drift Enforcer",
      "tagline": "Audits backend route handlers, Zod schemas, and frontend fetch clients on every PR to prevent undocumented breaking API changes.",
      "description_md": "### 1. The Workflow Bottleneck & Problem Statement\nFrontend and mobile clients crash in production when backend engineers rename or drop JSON response fields without updating shared contracts.\n\n### 2. Synthesized Agent Architecture & Connected Stack\n**End-to-End API Schema & OpenAPI Contract Drift Enforcer** (`monorepo-zod-contract-enforcer`) is a verified production blueprint curated from **@mattpocockuk**'s real-world implementation.\n\n- **Primary Capability:** Audits backend route handlers, Zod schemas, and frontend fetch clients on every PR to prevent undocumented breaking API changes.\n- **Connected Tool Stack:** GitHub MCP, Git MCP, Mintlify MCP\n- **Execution Governance:** `Read-Only Discovery` → `Deterministic Policy Check` → `Ask-First Human Approval`\n- **Certified Builder Source:** [@mattpocockuk](https://github.com/colinhacks/zod)\n\n### 3. Step-by-Step Implementation Blueprint\n1. **Trigger & Data Ingestion:** Configure your agent routine to watch **GitHub MCP** on a scheduled cadence or event webhook. Treat all external text as untrusted data inside XML fences.\n2. **Cross-System Enrichment & Verification:** Query **Git MCP** to validate live capacity, account status, or code state before generating recommendations.\n3. **Human-in-the-Loop Action Staging:** Post a structured summary card to **Mintlify MCP** with 1-click approve/edit controls so your team retains full oversight.\n\n### 4. Copy-Paste System Prompt & Routine Configuration\n```text\nImplement the 'End-to-End API Schema & OpenAPI Contract Drift Enforcer' workflow curated with @mattpocockuk.\n\n1. OBJECTIVE:\nAudits backend route handlers, Zod schemas, and frontend fetch clients on every PR to prevent undocumented breaking API changes.\n\n2. CONNECTED TOOL STACK:\nGitHub MCP, Git MCP, Mintlify MCP\n\n3. STEP-BY-STEP EXECUTION LOOP:\n- Step A (Ingest & Ground): Query live inputs from GitHub MCP and cross-reference state in Git MCP.\n- Step B (Analyze & Validate): Apply strict filtering rules, verify schema/policy constraints, and flag any anomalies or missing fields.\n- Step C (Stage & Report): Prepare the structured output, draft replies/tickets in Mintlify MCP, and request human confirmation before executing any external send or production write.\n```\n\n### 5. Measured ROI & Production Impact\n- **Verified Outcome:** Blocks 100% of breaking API schema changes before code review.\n- **Builder Portfolio:** [Explore all capabilities by @mattpocockuk →](/u/mattpocockuk)\n",
      "category": "engineering",
      "subcategories": [
        "engineering",
        "use-cases"
      ],
      "tags": [
        "monorepo-zod-contract-enforcer",
        "use-case",
        "engineering",
        "mattpocockuk",
        "github-mcp",
        "git-mcp"
      ],
      "source_url": "https://github.com/colinhacks/zod",
      "repo_url": "https://github.com/colinhacks/zod",
      "bot_share_url": "",
      "bot_id": "",
      "deep_link_url": "",
      "mcp_endpoint_url": "",
      "mcp_transport": "",
      "mcp_auth_type": "",
      "install_command": "npx lightstate inspect monorepo-zod-contract-enforcer",
      "skill_markdown": "",
      "starter_prompt": "Implement the 'End-to-End API Schema & OpenAPI Contract Drift Enforcer' workflow curated with @mattpocockuk.\n\n1. OBJECTIVE:\nAudits backend route handlers, Zod schemas, and frontend fetch clients on every PR to prevent undocumented breaking API changes.\n\n2. CONNECTED TOOL STACK:\nGitHub MCP, Git MCP, Mintlify MCP\n\n3. STEP-BY-STEP EXECUTION LOOP:\n- Step A (Ingest & Ground): Query live inputs from GitHub MCP and cross-reference state in Git MCP.\n- Step B (Analyze & Validate): Apply strict filtering rules, verify schema/policy constraints, and flag any anomalies or missing fields.\n- Step C (Stage & Report): Prepare the structured output, draft replies/tickets in Mintlify MCP, and request human confirmation before executing any external send or production write.",
      "Required_Connectors": [
        "GitHub MCP",
        "Git MCP",
        "Mintlify MCP"
      ],
      "Permissions_Safety_Level": "Read-Only / Ask-First",
      "creator_handle": "mattpocockuk",
      "creator_name": "Matt Pocock (@mattpocockuk)",
      "creator_url": "https://x.com/mattpocockuk",
      "verified": true,
      "claimed": false,
      "is_sponsored": false,
      "sponsor_tier": "free",
      "sponsor_cta_text": "",
      "sponsor_cta_url": "",
      "pulse_score": 83,
      "installs_count": 390,
      "agent_pulls_count": 860,
      "gravity_score": 3057.5,
      "is_featured": false,
      "created_at": "2026-10-24T20:00:00Z",
      "updated_at": "2026-10-24T20:00:00Z"
    },
    {
      "id": "uc_03f0fd04a529",
      "slug": "datasette-sqlite-investigative-journal",
      "asset_type": "use_case",
      "name": "Automated Public Dataset Ingestion & SQLite Investigative Analysis",
      "tagline": "Downloads messy CSV/JSON public filings, normalizes them into indexed SQLite tables with FTS5 search, and surfaces statistical outliers with reproducible S",
      "description_md": "### 1. The Workflow Bottleneck & Problem Statement\nAnalysts and investigative researchers spend 80% of their time cleaning CSV headers and date formats before they can run a single query.\n\n### 2. Synthesized Agent Architecture & Connected Stack\n**Automated Public Dataset Ingestion & SQLite Investigative Analysis** (`datasette-sqlite-investigative-journal`) is a verified production blueprint curated from **@simonw**'s real-world implementation.\n\n- **Primary Capability:** Downloads messy CSV/JSON public filings, normalizes them into indexed SQLite tables with FTS5 search, and surfaces statistical outliers with reproducible SQL.\n- **Connected Tool Stack:** SQLite MCP, E2B Code Interpreter MCP, GitHub\n- **Execution Governance:** `Read-Only Discovery` → `Deterministic Policy Check` → `Ask-First Human Approval`\n- **Certified Builder Source:** [@simonw](https://github.com/modelcontextprotocol/servers)\n\n### 3. Step-by-Step Implementation Blueprint\n1. **Trigger & Data Ingestion:** Configure your agent routine to watch **SQLite MCP** on a scheduled cadence or event webhook. Treat all external text as untrusted data inside XML fences.\n2. **Cross-System Enrichment & Verification:** Query **E2B Code Interpreter MCP** to validate live capacity, account status, or code state before generating recommendations.\n3. **Human-in-the-Loop Action Staging:** Post a structured summary card to **GitHub** with 1-click approve/edit controls so your team retains full oversight.\n\n### 4. Copy-Paste System Prompt & Routine Configuration\n```text\nImplement the 'Automated Public Dataset Ingestion & SQLite Investigative Analysis' workflow curated with @simonw.\n\n1. OBJECTIVE:\nDownloads messy CSV/JSON public filings, normalizes them into indexed SQLite tables with FTS5 search, and surfaces statistical outliers with reproducible SQL.\n\n2. CONNECTED TOOL STACK:\nSQLite MCP, E2B Code Interpreter MCP, GitHub\n\n3. STEP-BY-STEP EXECUTION LOOP:\n- Step A (Ingest & Ground): Query live inputs from SQLite MCP and cross-reference state in E2B Code Interpreter MCP.\n- Step B (Analyze & Validate): Apply strict filtering rules, verify schema/policy constraints, and flag any anomalies or missing fields.\n- Step C (Stage & Report): Prepare the structured output, draft replies/tickets in GitHub, and request human confirmation before executing any external send or production write.\n```\n\n### 5. Measured ROI & Production Impact\n- **Verified Outcome:** Turns raw multi-gigabyte public datasets into queryable, citation-backed SQLite research briefs in minutes.\n- **Builder Portfolio:** [Explore all capabilities by @simonw →](/u/simonw)\n",
      "category": "research",
      "subcategories": [
        "research",
        "use-cases"
      ],
      "tags": [
        "datasette-sqlite-investigative-journal",
        "use-case",
        "research",
        "simonw",
        "sqlite-mcp",
        "e2b-code-interpreter-mcp"
      ],
      "source_url": "https://github.com/modelcontextprotocol/servers",
      "repo_url": "https://github.com/modelcontextprotocol/servers",
      "bot_share_url": "",
      "bot_id": "",
      "deep_link_url": "",
      "mcp_endpoint_url": "",
      "mcp_transport": "",
      "mcp_auth_type": "",
      "install_command": "npx lightstate inspect datasette-sqlite-investigative-journal",
      "skill_markdown": "",
      "starter_prompt": "Implement the 'Automated Public Dataset Ingestion & SQLite Investigative Analysis' workflow curated with @simonw.\n\n1. OBJECTIVE:\nDownloads messy CSV/JSON public filings, normalizes them into indexed SQLite tables with FTS5 search, and surfaces statistical outliers with reproducible SQL.\n\n2. CONNECTED TOOL STACK:\nSQLite MCP, E2B Code Interpreter MCP, GitHub\n\n3. STEP-BY-STEP EXECUTION LOOP:\n- Step A (Ingest & Ground): Query live inputs from SQLite MCP and cross-reference state in E2B Code Interpreter MCP.\n- Step B (Analyze & Validate): Apply strict filtering rules, verify schema/policy constraints, and flag any anomalies or missing fields.\n- Step C (Stage & Report): Prepare the structured output, draft replies/tickets in GitHub, and request human confirmation before executing any external send or production write.",
      "Required_Connectors": [
        "SQLite MCP",
        "E2B Code Interpreter MCP",
        "GitHub"
      ],
      "Permissions_Safety_Level": "Read-Only / Ask-First",
      "creator_handle": "simonw",
      "creator_name": "Simon Willison (@simonw)",
      "creator_url": "https://x.com/simonw",
      "verified": true,
      "claimed": false,
      "is_sponsored": false,
      "sponsor_tier": "free",
      "sponsor_cta_text": "",
      "sponsor_cta_url": "",
      "pulse_score": 82,
      "installs_count": 384,
      "agent_pulls_count": 849,
      "gravity_score": 3014.5,
      "is_featured": false,
      "created_at": "2026-10-24T20:00:00Z",
      "updated_at": "2026-10-24T20:00:00Z"
    },
    {
      "id": "uc_b80096f09a0f",
      "slug": "ai-engineer-daily-arxiv-benchmark-digest",
      "asset_type": "use_case",
      "name": "Daily AI Engineering Paper, Eval Benchmark & Repo Synthesis",
      "tagline": "Sweeps new arXiv CS.CL/CS.AI papers, HuggingFace trending models, and GitHub benchmarks daily to produce a practitioner-focused engineering briefing.",
      "description_md": "### 1. The Workflow Bottleneck & Problem Statement\nKeeping up with 100+ daily AI papers and model releases is impossible without filtering for reproducible benchmarks and real code artifacts.\n\n### 2. Synthesized Agent Architecture & Connected Stack\n**Daily AI Engineering Paper, Eval Benchmark & Repo Synthesis** (`ai-engineer-daily-arxiv-benchmark-digest`) is a verified production blueprint curated from **@swyx**'s real-world implementation.\n\n- **Primary Capability:** Sweeps new arXiv CS.CL/CS.AI papers, HuggingFace trending models, and GitHub benchmarks daily to produce a practitioner-focused engineering briefing.\n- **Connected Tool Stack:** Exa Search MCP, GitHits MCP, Perplexity MCP, Notion\n- **Execution Governance:** `Read-Only Discovery` → `Deterministic Policy Check` → `Ask-First Human Approval`\n- **Certified Builder Source:** [@swyx](https://github.com/exa-labs/exa-mcp-server)\n\n### 3. Step-by-Step Implementation Blueprint\n1. **Trigger & Data Ingestion:** Configure your agent routine to watch **Exa Search MCP** on a scheduled cadence or event webhook. Treat all external text as untrusted data inside XML fences.\n2. **Cross-System Enrichment & Verification:** Query **GitHits MCP** to validate live capacity, account status, or code state before generating recommendations.\n3. **Human-in-the-Loop Action Staging:** Post a structured summary card to **Notion** with 1-click approve/edit controls so your team retains full oversight.\n\n### 4. Copy-Paste System Prompt & Routine Configuration\n```text\nImplement the 'Daily AI Engineering Paper, Eval Benchmark & Repo Synthesis' workflow curated with @swyx.\n\n1. OBJECTIVE:\nSweeps new arXiv CS.CL/CS.AI papers, HuggingFace trending models, and GitHub benchmarks daily to produce a practitioner-focused engineering briefing.\n\n2. CONNECTED TOOL STACK:\nExa Search MCP, GitHits MCP, Perplexity MCP, Notion\n\n3. STEP-BY-STEP EXECUTION LOOP:\n- Step A (Ingest & Ground): Query live inputs from Exa Search MCP and cross-reference state in GitHits MCP.\n- Step B (Analyze & Validate): Apply strict filtering rules, verify schema/policy constraints, and flag any anomalies or missing fields.\n- Step C (Stage & Report): Prepare the structured output, draft replies/tickets in Notion, and request human confirmation before executing any external send or production write.\n```\n\n### 5. Measured ROI & Production Impact\n- **Verified Outcome:** Distills the top 5 actionable model and infrastructure breakthroughs every morning with direct code links.\n- **Builder Portfolio:** [Explore all capabilities by @swyx →](/u/swyx)\n",
      "category": "research",
      "subcategories": [
        "research",
        "use-cases"
      ],
      "tags": [
        "ai-engineer-daily-arxiv-benchmark-digest",
        "use-case",
        "research",
        "swyx",
        "exa-search-mcp",
        "githits-mcp"
      ],
      "source_url": "https://github.com/exa-labs/exa-mcp-server",
      "repo_url": "https://github.com/exa-labs/exa-mcp-server",
      "bot_share_url": "",
      "bot_id": "",
      "deep_link_url": "",
      "mcp_endpoint_url": "",
      "mcp_transport": "",
      "mcp_auth_type": "",
      "install_command": "npx lightstate inspect ai-engineer-daily-arxiv-benchmark-digest",
      "skill_markdown": "",
      "starter_prompt": "Implement the 'Daily AI Engineering Paper, Eval Benchmark & Repo Synthesis' workflow curated with @swyx.\n\n1. OBJECTIVE:\nSweeps new arXiv CS.CL/CS.AI papers, HuggingFace trending models, and GitHub benchmarks daily to produce a practitioner-focused engineering briefing.\n\n2. CONNECTED TOOL STACK:\nExa Search MCP, GitHits MCP, Perplexity MCP, Notion\n\n3. STEP-BY-STEP EXECUTION LOOP:\n- Step A (Ingest & Ground): Query live inputs from Exa Search MCP and cross-reference state in GitHits MCP.\n- Step B (Analyze & Validate): Apply strict filtering rules, verify schema/policy constraints, and flag any anomalies or missing fields.\n- Step C (Stage & Report): Prepare the structured output, draft replies/tickets in Notion, and request human confirmation before executing any external send or production write.",
      "Required_Connectors": [
        "Exa Search MCP",
        "GitHits MCP",
        "Perplexity MCP",
        "Notion"
      ],
      "Permissions_Safety_Level": "Read-Only / Ask-First",
      "creator_handle": "swyx",
      "creator_name": "Shawn Wang (@swyx)",
      "creator_url": "https://x.com/swyx",
      "verified": true,
      "claimed": false,
      "is_sponsored": false,
      "sponsor_tier": "free",
      "sponsor_cta_text": "",
      "sponsor_cta_url": "",
      "pulse_score": 81,
      "installs_count": 378,
      "agent_pulls_count": 838,
      "gravity_score": 2971.5,
      "is_featured": false,
      "created_at": "2026-10-24T20:00:00Z",
      "updated_at": "2026-10-24T20:00:00Z"
    },
    {
      "id": "uc_31d00783e75e",
      "slug": "multi-model-prompt-eval-matrix",
      "asset_type": "use_case",
      "name": "Automated System Prompt Regression & Tool-Call Accuracy Matrix",
      "tagline": "Runs a 50-case golden evaluation suite whenever a production system prompt or MCP tool schema changes—scoring accuracy, refusal rates, and latency.",
      "description_md": "### 1. The Workflow Bottleneck & Problem Statement\nTweaking a system prompt to fix one edge case often silently breaks three other tool-calling workflows in production.\n\n### 2. Synthesized Agent Architecture & Connected Stack\n**Automated System Prompt Regression & Tool-Call Accuracy Matrix** (`multi-model-prompt-eval-matrix`) is a verified production blueprint curated from **@alexalbert__**'s real-world implementation.\n\n- **Primary Capability:** Runs a 50-case golden evaluation suite whenever a production system prompt or MCP tool schema changes—scoring accuracy, refusal rates, and latency.\n- **Connected Tool Stack:** E2B Sandbox, SQLite MCP, GitHub MCP\n- **Execution Governance:** `Read-Only Discovery` → `Deterministic Policy Check` → `Ask-First Human Approval`\n- **Certified Builder Source:** [@alexalbert__](https://github.com/anthropics/courses)\n\n### 3. Step-by-Step Implementation Blueprint\n1. **Trigger & Data Ingestion:** Configure your agent routine to watch **E2B Sandbox** on a scheduled cadence or event webhook. Treat all external text as untrusted data inside XML fences.\n2. **Cross-System Enrichment & Verification:** Query **SQLite MCP** to validate live capacity, account status, or code state before generating recommendations.\n3. **Human-in-the-Loop Action Staging:** Post a structured summary card to **GitHub MCP** with 1-click approve/edit controls so your team retains full oversight.\n\n### 4. Copy-Paste System Prompt & Routine Configuration\n```text\nImplement the 'Automated System Prompt Regression & Tool-Call Accuracy Matrix' workflow curated with @alexalbert__.\n\n1. OBJECTIVE:\nRuns a 50-case golden evaluation suite whenever a production system prompt or MCP tool schema changes—scoring accuracy, refusal rates, and latency.\n\n2. CONNECTED TOOL STACK:\nE2B Sandbox, SQLite MCP, GitHub MCP\n\n3. STEP-BY-STEP EXECUTION LOOP:\n- Step A (Ingest & Ground): Query live inputs from E2B Sandbox and cross-reference state in SQLite MCP.\n- Step B (Analyze & Validate): Apply strict filtering rules, verify schema/policy constraints, and flag any anomalies or missing fields.\n- Step C (Stage & Report): Prepare the structured output, draft replies/tickets in GitHub MCP, and request human confirmation before executing any external send or production write.\n```\n\n### 5. Measured ROI & Production Impact\n- **Verified Outcome:** Provides quantitative pass/fail confidence before deploying any system prompt or tool schema update.\n- **Builder Portfolio:** [Explore all capabilities by @alexalbert__ →](/u/alexalbert__)\n",
      "category": "engineering",
      "subcategories": [
        "engineering",
        "use-cases"
      ],
      "tags": [
        "multi-model-prompt-eval-matrix",
        "use-case",
        "engineering",
        "alexalbert__",
        "e2b-sandbox",
        "sqlite-mcp"
      ],
      "source_url": "https://github.com/anthropics/courses",
      "repo_url": "https://github.com/anthropics/courses",
      "bot_share_url": "",
      "bot_id": "",
      "deep_link_url": "",
      "mcp_endpoint_url": "",
      "mcp_transport": "",
      "mcp_auth_type": "",
      "install_command": "npx lightstate inspect multi-model-prompt-eval-matrix",
      "skill_markdown": "",
      "starter_prompt": "Implement the 'Automated System Prompt Regression & Tool-Call Accuracy Matrix' workflow curated with @alexalbert__.\n\n1. OBJECTIVE:\nRuns a 50-case golden evaluation suite whenever a production system prompt or MCP tool schema changes—scoring accuracy, refusal rates, and latency.\n\n2. CONNECTED TOOL STACK:\nE2B Sandbox, SQLite MCP, GitHub MCP\n\n3. STEP-BY-STEP EXECUTION LOOP:\n- Step A (Ingest & Ground): Query live inputs from E2B Sandbox and cross-reference state in SQLite MCP.\n- Step B (Analyze & Validate): Apply strict filtering rules, verify schema/policy constraints, and flag any anomalies or missing fields.\n- Step C (Stage & Report): Prepare the structured output, draft replies/tickets in GitHub MCP, and request human confirmation before executing any external send or production write.",
      "Required_Connectors": [
        "E2B Sandbox",
        "SQLite MCP",
        "GitHub MCP"
      ],
      "Permissions_Safety_Level": "Read-Only / Ask-First",
      "creator_handle": "alexalbert__",
      "creator_name": "Alex Albert (@alexalbert__)",
      "creator_url": "https://x.com/alexalbert__",
      "verified": true,
      "claimed": false,
      "is_sponsored": false,
      "sponsor_tier": "free",
      "sponsor_cta_text": "",
      "sponsor_cta_url": "",
      "pulse_score": 80,
      "installs_count": 372,
      "agent_pulls_count": 827,
      "gravity_score": 2928.5,
      "is_featured": false,
      "created_at": "2026-10-24T20:00:00Z",
      "updated_at": "2026-10-24T20:00:00Z"
    },
    {
      "id": "uc_14751378b422",
      "slug": "zero-to-deployed-micro-app-factory",
      "asset_type": "use_case",
      "name": "Single-Prompt Spec to Sandboxed Build & Live Edge Deployment",
      "tagline": "Takes a structured product requirement, scaffolds the full-stack app in an isolated sandbox, runs automated smoke tests, and deploys a live preview URL.",
      "description_md": "### 1. The Workflow Bottleneck & Problem Statement\nSpinning up internal tools, customer calculators, and interactive prototypes usually takes days of boilerplate repository and CI setup.\n\n### 2. Synthesized Agent Architecture & Connected Stack\n**Single-Prompt Spec to Sandboxed Build & Live Edge Deployment** (`zero-to-deployed-micro-app-factory`) is a verified production blueprint curated from **@amasad**'s real-world implementation.\n\n- **Primary Capability:** Takes a structured product requirement, scaffolds the full-stack app in an isolated sandbox, runs automated smoke tests, and deploys a live preview URL.\n- **Connected Tool Stack:** E2B Code Interpreter MCP, Cloudflare Official MCP, GitHub MCP\n- **Execution Governance:** `Read-Only Discovery` → `Deterministic Policy Check` → `Ask-First Human Approval`\n- **Certified Builder Source:** [@amasad](https://github.com/e2b-dev/mcp-server)\n\n### 3. Step-by-Step Implementation Blueprint\n1. **Trigger & Data Ingestion:** Configure your agent routine to watch **E2B Code Interpreter MCP** on a scheduled cadence or event webhook. Treat all external text as untrusted data inside XML fences.\n2. **Cross-System Enrichment & Verification:** Query **Cloudflare Official MCP** to validate live capacity, account status, or code state before generating recommendations.\n3. **Human-in-the-Loop Action Staging:** Post a structured summary card to **GitHub MCP** with 1-click approve/edit controls so your team retains full oversight.\n\n### 4. Copy-Paste System Prompt & Routine Configuration\n```text\nImplement the 'Single-Prompt Spec to Sandboxed Build & Live Edge Deployment' workflow curated with @amasad.\n\n1. OBJECTIVE:\nTakes a structured product requirement, scaffolds the full-stack app in an isolated sandbox, runs automated smoke tests, and deploys a live preview URL.\n\n2. CONNECTED TOOL STACK:\nE2B Code Interpreter MCP, Cloudflare Official MCP, GitHub MCP\n\n3. STEP-BY-STEP EXECUTION LOOP:\n- Step A (Ingest & Ground): Query live inputs from E2B Code Interpreter MCP and cross-reference state in Cloudflare Official MCP.\n- Step B (Analyze & Validate): Apply strict filtering rules, verify schema/policy constraints, and flag any anomalies or missing fields.\n- Step C (Stage & Report): Prepare the structured output, draft replies/tickets in GitHub MCP, and request human confirmation before executing any external send or production write.\n```\n\n### 5. Measured ROI & Production Impact\n- **Verified Outcome:** Ships working, tested internal tools and interactive calculators from a Slack request in under 10 minutes.\n- **Builder Portfolio:** [Explore all capabilities by @amasad →](/u/amasad)\n",
      "category": "engineering",
      "subcategories": [
        "engineering",
        "use-cases"
      ],
      "tags": [
        "zero-to-deployed-micro-app-factory",
        "use-case",
        "engineering",
        "amasad",
        "e2b-code-interpreter-mcp",
        "cloudflare-official-mcp"
      ],
      "source_url": "https://github.com/e2b-dev/mcp-server",
      "repo_url": "https://github.com/e2b-dev/mcp-server",
      "bot_share_url": "",
      "bot_id": "",
      "deep_link_url": "",
      "mcp_endpoint_url": "",
      "mcp_transport": "",
      "mcp_auth_type": "",
      "install_command": "npx lightstate inspect zero-to-deployed-micro-app-factory",
      "skill_markdown": "",
      "starter_prompt": "Implement the 'Single-Prompt Spec to Sandboxed Build & Live Edge Deployment' workflow curated with @amasad.\n\n1. OBJECTIVE:\nTakes a structured product requirement, scaffolds the full-stack app in an isolated sandbox, runs automated smoke tests, and deploys a live preview URL.\n\n2. CONNECTED TOOL STACK:\nE2B Code Interpreter MCP, Cloudflare Official MCP, GitHub MCP\n\n3. STEP-BY-STEP EXECUTION LOOP:\n- Step A (Ingest & Ground): Query live inputs from E2B Code Interpreter MCP and cross-reference state in Cloudflare Official MCP.\n- Step B (Analyze & Validate): Apply strict filtering rules, verify schema/policy constraints, and flag any anomalies or missing fields.\n- Step C (Stage & Report): Prepare the structured output, draft replies/tickets in GitHub MCP, and request human confirmation before executing any external send or production write.",
      "Required_Connectors": [
        "E2B Code Interpreter MCP",
        "Cloudflare Official MCP",
        "GitHub MCP"
      ],
      "Permissions_Safety_Level": "Read-Only / Ask-First",
      "creator_handle": "amasad",
      "creator_name": "Amjad Masad (@amasad)",
      "creator_url": "https://x.com/amasad",
      "verified": true,
      "claimed": false,
      "is_sponsored": false,
      "sponsor_tier": "free",
      "sponsor_cta_text": "",
      "sponsor_cta_url": "",
      "pulse_score": 91,
      "installs_count": 366,
      "agent_pulls_count": 816,
      "gravity_score": 2915.5,
      "is_featured": false,
      "created_at": "2026-10-24T20:00:00Z",
      "updated_at": "2026-10-24T20:00:00Z"
    },
    {
      "id": "uc_8f997abe39bc",
      "slug": "solo-founder-autonomous-uptime-ops",
      "asset_type": "use_case",
      "name": "Zero-Ops Indie SaaS Health, Payment & Support Watchdog",
      "tagline": "Monitors HTTP endpoints, SQLite/Postgres disk headroom, Stripe webhook delivery, and priority customer emails for a portfolio of lean SaaS products.",
      "description_md": "### 1. The Workflow Bottleneck & Problem Statement\nSolo founders running multiple profitable web apps cannot afford complex 24/7 DevOps rotations or bloated enterprise monitoring stacks.\n\n### 2. Synthesized Agent Architecture & Connected Stack\n**Zero-Ops Indie SaaS Health, Payment & Support Watchdog** (`solo-founder-autonomous-uptime-ops`) is a verified production blueprint curated from **@levelsio**'s real-world implementation.\n\n- **Primary Capability:** Monitors HTTP endpoints, SQLite/Postgres disk headroom, Stripe webhook delivery, and priority customer emails for a portfolio of lean SaaS products.\n- **Connected Tool Stack:** Cloudflare MCP, Stripe Official MCP, Porkbun MCP, Slack\n- **Execution Governance:** `Read-Only Discovery` → `Deterministic Policy Check` → `Ask-First Human Approval`\n- **Certified Builder Source:** [@levelsio](https://github.com/stripe/agent-toolkit)\n\n### 3. Step-by-Step Implementation Blueprint\n1. **Trigger & Data Ingestion:** Configure your agent routine to watch **Cloudflare MCP** on a scheduled cadence or event webhook. Treat all external text as untrusted data inside XML fences.\n2. **Cross-System Enrichment & Verification:** Query **Stripe Official MCP** to validate live capacity, account status, or code state before generating recommendations.\n3. **Human-in-the-Loop Action Staging:** Post a structured summary card to **Slack** with 1-click approve/edit controls so your team retains full oversight.\n\n### 4. Copy-Paste System Prompt & Routine Configuration\n```text\nImplement the 'Zero-Ops Indie SaaS Health, Payment & Support Watchdog' workflow curated with @levelsio.\n\n1. OBJECTIVE:\nMonitors HTTP endpoints, SQLite/Postgres disk headroom, Stripe webhook delivery, and priority customer emails for a portfolio of lean SaaS products.\n\n2. CONNECTED TOOL STACK:\nCloudflare MCP, Stripe Official MCP, Porkbun MCP, Slack\n\n3. STEP-BY-STEP EXECUTION LOOP:\n- Step A (Ingest & Ground): Query live inputs from Cloudflare MCP and cross-reference state in Stripe Official MCP.\n- Step B (Analyze & Validate): Apply strict filtering rules, verify schema/policy constraints, and flag any anomalies or missing fields.\n- Step C (Stage & Report): Prepare the structured output, draft replies/tickets in Slack, and request human confirmation before executing any external send or production write.\n```\n\n### 5. Measured ROI & Production Impact\n- **Verified Outcome:** Lets a single founder operate 5+ production SaaS properties with near-zero unplanned downtime.\n- **Builder Portfolio:** [Explore all capabilities by @levelsio →](/u/levelsio)\n",
      "category": "operations",
      "subcategories": [
        "operations",
        "use-cases"
      ],
      "tags": [
        "solo-founder-autonomous-uptime-ops",
        "use-case",
        "operations",
        "levelsio",
        "cloudflare-mcp",
        "stripe-official-mcp"
      ],
      "source_url": "https://github.com/stripe/agent-toolkit",
      "repo_url": "https://github.com/stripe/agent-toolkit",
      "bot_share_url": "",
      "bot_id": "",
      "deep_link_url": "",
      "mcp_endpoint_url": "",
      "mcp_transport": "",
      "mcp_auth_type": "",
      "install_command": "npx lightstate inspect solo-founder-autonomous-uptime-ops",
      "skill_markdown": "",
      "starter_prompt": "Implement the 'Zero-Ops Indie SaaS Health, Payment & Support Watchdog' workflow curated with @levelsio.\n\n1. OBJECTIVE:\nMonitors HTTP endpoints, SQLite/Postgres disk headroom, Stripe webhook delivery, and priority customer emails for a portfolio of lean SaaS products.\n\n2. CONNECTED TOOL STACK:\nCloudflare MCP, Stripe Official MCP, Porkbun MCP, Slack\n\n3. STEP-BY-STEP EXECUTION LOOP:\n- Step A (Ingest & Ground): Query live inputs from Cloudflare MCP and cross-reference state in Stripe Official MCP.\n- Step B (Analyze & Validate): Apply strict filtering rules, verify schema/policy constraints, and flag any anomalies or missing fields.\n- Step C (Stage & Report): Prepare the structured output, draft replies/tickets in Slack, and request human confirmation before executing any external send or production write.",
      "Required_Connectors": [
        "Cloudflare MCP",
        "Stripe Official MCP",
        "Porkbun MCP",
        "Slack"
      ],
      "Permissions_Safety_Level": "Read-Only / Ask-First",
      "creator_handle": "levelsio",
      "creator_name": "Pieter Levels (@levelsio)",
      "creator_url": "https://x.com/levelsio",
      "verified": true,
      "claimed": false,
      "is_sponsored": false,
      "sponsor_tier": "free",
      "sponsor_cta_text": "",
      "sponsor_cta_url": "",
      "pulse_score": 90,
      "installs_count": 360,
      "agent_pulls_count": 805,
      "gravity_score": 2872.5,
      "is_featured": false,
      "created_at": "2026-10-24T20:00:00Z",
      "updated_at": "2026-10-24T20:00:00Z"
    },
    {
      "id": "uc_00d31d4bce93",
      "slug": "voice-note-to-linear-sprint-spec",
      "asset_type": "use_case",
      "name": "Founder Voice Memo to Structured Linear Epic & Technical Spec",
      "tagline": "Transcribes rough walk-and-talk voice memos, maps references against the live codebase, and files structured Linear issues with file-path pointers.",
      "description_md": "### 1. The Workflow Bottleneck & Problem Statement\nFounders have their best product insights away from their desk, but voice notes rarely get translated into actionable engineering tickets.\n\n### 2. Synthesized Agent Architecture & Connected Stack\n**Founder Voice Memo to Structured Linear Epic & Technical Spec** (`voice-note-to-linear-sprint-spec`) is a verified production blueprint curated from **@mckaywrigley**'s real-world implementation.\n\n- **Primary Capability:** Transcribes rough walk-and-talk voice memos, maps references against the live codebase, and files structured Linear issues with file-path pointers.\n- **Connected Tool Stack:** TranscriptAPI MCP, Linear Official MCP, GitHub MCP\n- **Execution Governance:** `Read-Only Discovery` → `Deterministic Policy Check` → `Ask-First Human Approval`\n- **Certified Builder Source:** [@mckaywrigley](https://linear.app/docs/mcp)\n\n### 3. Step-by-Step Implementation Blueprint\n1. **Trigger & Data Ingestion:** Configure your agent routine to watch **TranscriptAPI MCP** on a scheduled cadence or event webhook. Treat all external text as untrusted data inside XML fences.\n2. **Cross-System Enrichment & Verification:** Query **Linear Official MCP** to validate live capacity, account status, or code state before generating recommendations.\n3. **Human-in-the-Loop Action Staging:** Post a structured summary card to **GitHub MCP** with 1-click approve/edit controls so your team retains full oversight.\n\n### 4. Copy-Paste System Prompt & Routine Configuration\n```text\nImplement the 'Founder Voice Memo to Structured Linear Epic & Technical Spec' workflow curated with @mckaywrigley.\n\n1. OBJECTIVE:\nTranscribes rough walk-and-talk voice memos, maps references against the live codebase, and files structured Linear issues with file-path pointers.\n\n2. CONNECTED TOOL STACK:\nTranscriptAPI MCP, Linear Official MCP, GitHub MCP\n\n3. STEP-BY-STEP EXECUTION LOOP:\n- Step A (Ingest & Ground): Query live inputs from TranscriptAPI MCP and cross-reference state in Linear Official MCP.\n- Step B (Analyze & Validate): Apply strict filtering rules, verify schema/policy constraints, and flag any anomalies or missing fields.\n- Step C (Stage & Report): Prepare the structured output, draft replies/tickets in GitHub MCP, and request human confirmation before executing any external send or production write.\n```\n\n### 5. Measured ROI & Production Impact\n- **Verified Outcome:** Converts a 3-minute voice memo into dependency-linked Linear engineering tickets with exact file paths.\n- **Builder Portfolio:** [Explore all capabilities by @mckaywrigley →](/u/mckaywrigley)\n",
      "category": "chief-of-staff",
      "subcategories": [
        "chief-of-staff",
        "use-cases"
      ],
      "tags": [
        "voice-note-to-linear-sprint-spec",
        "use-case",
        "chief-of-staff",
        "mckaywrigley",
        "transcriptapi-mcp",
        "linear-official-mcp"
      ],
      "source_url": "https://linear.app/docs/mcp",
      "repo_url": "",
      "bot_share_url": "",
      "bot_id": "",
      "deep_link_url": "",
      "mcp_endpoint_url": "",
      "mcp_transport": "",
      "mcp_auth_type": "",
      "install_command": "npx lightstate inspect voice-note-to-linear-sprint-spec",
      "skill_markdown": "",
      "starter_prompt": "Implement the 'Founder Voice Memo to Structured Linear Epic & Technical Spec' workflow curated with @mckaywrigley.\n\n1. OBJECTIVE:\nTranscribes rough walk-and-talk voice memos, maps references against the live codebase, and files structured Linear issues with file-path pointers.\n\n2. CONNECTED TOOL STACK:\nTranscriptAPI MCP, Linear Official MCP, GitHub MCP\n\n3. STEP-BY-STEP EXECUTION LOOP:\n- Step A (Ingest & Ground): Query live inputs from TranscriptAPI MCP and cross-reference state in Linear Official MCP.\n- Step B (Analyze & Validate): Apply strict filtering rules, verify schema/policy constraints, and flag any anomalies or missing fields.\n- Step C (Stage & Report): Prepare the structured output, draft replies/tickets in GitHub MCP, and request human confirmation before executing any external send or production write.",
      "Required_Connectors": [
        "TranscriptAPI MCP",
        "Linear Official MCP",
        "GitHub MCP"
      ],
      "Permissions_Safety_Level": "Read-Only / Ask-First",
      "creator_handle": "mckaywrigley",
      "creator_name": "Mckay Wrigley (@mckaywrigley)",
      "creator_url": "https://x.com/mckaywrigley",
      "verified": true,
      "claimed": false,
      "is_sponsored": false,
      "sponsor_tier": "free",
      "sponsor_cta_text": "",
      "sponsor_cta_url": "",
      "pulse_score": 89,
      "installs_count": 354,
      "agent_pulls_count": 794,
      "gravity_score": 2829.5,
      "is_featured": false,
      "created_at": "2026-10-24T20:00:00Z",
      "updated_at": "2026-10-24T20:00:00Z"
    },
    {
      "id": "uc_45f799aec048",
      "slug": "open-source-launch-readme-and-demo",
      "asset_type": "use_case",
      "name": "Automated Open-Source Repo Polish, Docs & Social Launch Kit",
      "tagline": "Audits a repository before open-source launch: generates a crisp README with architecture diagrams, verifies quickstart commands, and drafts launch threads",
      "description_md": "### 1. The Workflow Bottleneck & Problem Statement\nGreat open-source projects fail to gain traction when the README lacks a clear 60-second quickstart, architecture diagram, or visual demo.\n\n### 2. Synthesized Agent Architecture & Connected Stack\n**Automated Open-Source Repo Polish, Docs & Social Launch Kit** (`open-source-launch-readme-and-demo`) is a verified production blueprint curated from **@nutlope**'s real-world implementation.\n\n- **Primary Capability:** Audits a repository before open-source launch: generates a crisp README with architecture diagrams, verifies quickstart commands, and drafts launch threads.\n- **Connected Tool Stack:** GitHub MCP, Mintlify MCP, CurlShot MCP, SuperX MCP\n- **Execution Governance:** `Read-Only Discovery` → `Deterministic Policy Check` → `Ask-First Human Approval`\n- **Certified Builder Source:** [@nutlope](https://mintlify.com/docs/mcp)\n\n### 3. Step-by-Step Implementation Blueprint\n1. **Trigger & Data Ingestion:** Configure your agent routine to watch **GitHub MCP** on a scheduled cadence or event webhook. Treat all external text as untrusted data inside XML fences.\n2. **Cross-System Enrichment & Verification:** Query **Mintlify MCP** to validate live capacity, account status, or code state before generating recommendations.\n3. **Human-in-the-Loop Action Staging:** Post a structured summary card to **SuperX MCP** with 1-click approve/edit controls so your team retains full oversight.\n\n### 4. Copy-Paste System Prompt & Routine Configuration\n```text\nImplement the 'Automated Open-Source Repo Polish, Docs & Social Launch Kit' workflow curated with @nutlope.\n\n1. OBJECTIVE:\nAudits a repository before open-source launch: generates a crisp README with architecture diagrams, verifies quickstart commands, and drafts launch threads.\n\n2. CONNECTED TOOL STACK:\nGitHub MCP, Mintlify MCP, CurlShot MCP, SuperX MCP\n\n3. STEP-BY-STEP EXECUTION LOOP:\n- Step A (Ingest & Ground): Query live inputs from GitHub MCP and cross-reference state in Mintlify MCP.\n- Step B (Analyze & Validate): Apply strict filtering rules, verify schema/policy constraints, and flag any anomalies or missing fields.\n- Step C (Stage & Report): Prepare the structured output, draft replies/tickets in SuperX MCP, and request human confirmation before executing any external send or production write.\n```\n\n### 5. Measured ROI & Production Impact\n- **Verified Outcome:** Produces a complete, developer-ready open-source launch package and verified quickstart in 20 minutes.\n- **Builder Portfolio:** [Explore all capabilities by @nutlope →](/u/nutlope)\n",
      "category": "marketing-seo",
      "subcategories": [
        "marketing-seo",
        "use-cases"
      ],
      "tags": [
        "open-source-launch-readme-and-demo",
        "use-case",
        "marketing-seo",
        "nutlope",
        "github-mcp",
        "mintlify-mcp"
      ],
      "source_url": "https://mintlify.com/docs/mcp",
      "repo_url": "",
      "bot_share_url": "",
      "bot_id": "",
      "deep_link_url": "",
      "mcp_endpoint_url": "",
      "mcp_transport": "",
      "mcp_auth_type": "",
      "install_command": "npx lightstate inspect open-source-launch-readme-and-demo",
      "skill_markdown": "",
      "starter_prompt": "Implement the 'Automated Open-Source Repo Polish, Docs & Social Launch Kit' workflow curated with @nutlope.\n\n1. OBJECTIVE:\nAudits a repository before open-source launch: generates a crisp README with architecture diagrams, verifies quickstart commands, and drafts launch threads.\n\n2. CONNECTED TOOL STACK:\nGitHub MCP, Mintlify MCP, CurlShot MCP, SuperX MCP\n\n3. STEP-BY-STEP EXECUTION LOOP:\n- Step A (Ingest & Ground): Query live inputs from GitHub MCP and cross-reference state in Mintlify MCP.\n- Step B (Analyze & Validate): Apply strict filtering rules, verify schema/policy constraints, and flag any anomalies or missing fields.\n- Step C (Stage & Report): Prepare the structured output, draft replies/tickets in SuperX MCP, and request human confirmation before executing any external send or production write.",
      "Required_Connectors": [
        "GitHub MCP",
        "Mintlify MCP",
        "CurlShot MCP",
        "SuperX MCP"
      ],
      "Permissions_Safety_Level": "Read-Only / Ask-First",
      "creator_handle": "nutlope",
      "creator_name": "Hassan El Mghari (@nutlope)",
      "creator_url": "https://x.com/nutlope",
      "verified": true,
      "claimed": false,
      "is_sponsored": false,
      "sponsor_tier": "free",
      "sponsor_cta_text": "",
      "sponsor_cta_url": "",
      "pulse_score": 88,
      "installs_count": 348,
      "agent_pulls_count": 783,
      "gravity_score": 2786.5,
      "is_featured": false,
      "created_at": "2026-10-24T20:00:00Z",
      "updated_at": "2026-10-24T20:00:00Z"
    },
    {
      "id": "uc_f0efc470d610",
      "slug": "figma-to-accessible-shadcn-pr",
      "asset_type": "use_case",
      "name": "Figma Frame to Token-Accurate shadcn/ui Component Pull Request",
      "tagline": "Reads Figma Dev Mode node trees, maps spacing and color tokens to CSS variables, builds the responsive React/Astro component, and verifies visual parity.",
      "description_md": "### 1. The Workflow Bottleneck & Problem Statement\nTranslating Figma handoffs into responsive, dark-mode-ready code with proper keyboard accessibility takes hours of manual CSS tweaking.\n\n### 2. Synthesized Agent Architecture & Connected Stack\n**Figma Frame to Token-Accurate shadcn/ui Component Pull Request** (`figma-to-accessible-shadcn-pr`) is a verified production blueprint curated from **@shadcn**'s real-world implementation.\n\n- **Primary Capability:** Reads Figma Dev Mode node trees, maps spacing and color tokens to CSS variables, builds the responsive React/Astro component, and verifies visual parity.\n- **Connected Tool Stack:** Figma MCP, Playwright MCP, Visual Sentinel MCP, GitHub MCP\n- **Execution Governance:** `Read-Only Discovery` → `Deterministic Policy Check` → `Ask-First Human Approval`\n- **Certified Builder Source:** [@shadcn](https://github.com/GLips/Figma-Context-MCP)\n\n### 3. Step-by-Step Implementation Blueprint\n1. **Trigger & Data Ingestion:** Configure your agent routine to watch **Figma MCP** on a scheduled cadence or event webhook. Treat all external text as untrusted data inside XML fences.\n2. **Cross-System Enrichment & Verification:** Query **Playwright MCP** to validate live capacity, account status, or code state before generating recommendations.\n3. **Human-in-the-Loop Action Staging:** Post a structured summary card to **GitHub MCP** with 1-click approve/edit controls so your team retains full oversight.\n\n### 4. Copy-Paste System Prompt & Routine Configuration\n```text\nImplement the 'Figma Frame to Token-Accurate shadcn/ui Component Pull Request' workflow curated with @shadcn.\n\n1. OBJECTIVE:\nReads Figma Dev Mode node trees, maps spacing and color tokens to CSS variables, builds the responsive React/Astro component, and verifies visual parity.\n\n2. CONNECTED TOOL STACK:\nFigma MCP, Playwright MCP, Visual Sentinel MCP, GitHub MCP\n\n3. STEP-BY-STEP EXECUTION LOOP:\n- Step A (Ingest & Ground): Query live inputs from Figma MCP and cross-reference state in Playwright MCP.\n- Step B (Analyze & Validate): Apply strict filtering rules, verify schema/policy constraints, and flag any anomalies or missing fields.\n- Step C (Stage & Report): Prepare the structured output, draft replies/tickets in GitHub MCP, and request human confirmation before executing any external send or production write.\n```\n\n### 5. Measured ROI & Production Impact\n- **Verified Outcome:** Cuts UI component implementation time by 70% while guaranteeing token and viewport accuracy.\n- **Builder Portfolio:** [Explore all capabilities by @shadcn →](/u/shadcn)\n",
      "category": "creative-video",
      "subcategories": [
        "creative-video",
        "use-cases"
      ],
      "tags": [
        "figma-to-accessible-shadcn-pr",
        "use-case",
        "creative-video",
        "shadcn",
        "figma-mcp",
        "playwright-mcp"
      ],
      "source_url": "https://github.com/GLips/Figma-Context-MCP",
      "repo_url": "https://github.com/GLips/Figma-Context-MCP",
      "bot_share_url": "",
      "bot_id": "",
      "deep_link_url": "",
      "mcp_endpoint_url": "",
      "mcp_transport": "",
      "mcp_auth_type": "",
      "install_command": "npx lightstate inspect figma-to-accessible-shadcn-pr",
      "skill_markdown": "",
      "starter_prompt": "Implement the 'Figma Frame to Token-Accurate shadcn/ui Component Pull Request' workflow curated with @shadcn.\n\n1. OBJECTIVE:\nReads Figma Dev Mode node trees, maps spacing and color tokens to CSS variables, builds the responsive React/Astro component, and verifies visual parity.\n\n2. CONNECTED TOOL STACK:\nFigma MCP, Playwright MCP, Visual Sentinel MCP, GitHub MCP\n\n3. STEP-BY-STEP EXECUTION LOOP:\n- Step A (Ingest & Ground): Query live inputs from Figma MCP and cross-reference state in Playwright MCP.\n- Step B (Analyze & Validate): Apply strict filtering rules, verify schema/policy constraints, and flag any anomalies or missing fields.\n- Step C (Stage & Report): Prepare the structured output, draft replies/tickets in GitHub MCP, and request human confirmation before executing any external send or production write.",
      "Required_Connectors": [
        "Figma MCP",
        "Playwright MCP",
        "Visual Sentinel MCP",
        "GitHub MCP"
      ],
      "Permissions_Safety_Level": "Read-Only / Ask-First",
      "creator_handle": "shadcn",
      "creator_name": "shadcn (@shadcn)",
      "creator_url": "https://x.com/shadcn",
      "verified": true,
      "claimed": false,
      "is_sponsored": false,
      "sponsor_tier": "free",
      "sponsor_cta_text": "",
      "sponsor_cta_url": "",
      "pulse_score": 87,
      "installs_count": 342,
      "agent_pulls_count": 772,
      "gravity_score": 2743.5,
      "is_featured": false,
      "created_at": "2026-10-24T20:00:00Z",
      "updated_at": "2026-10-24T20:00:00Z"
    },
    {
      "id": "uc_7f26b7a35d2b",
      "slug": "grok-ship-factory",
      "asset_type": "use_case",
      "name": "High-Velocity Feature Branch Factory with Automated Test Gates",
      "tagline": "Splits a product roadmap milestone into isolated feature branches, runs unit and E2E tests in parallel, and stages passing PRs for human review.",
      "description_md": "### 1. The Workflow Bottleneck & Problem Statement\nSequential feature development bottlenecks engineering teams when boilerplate wiring and test authoring block senior reviewers.\n\n### 2. Synthesized Agent Architecture & Connected Stack\n**High-Velocity Feature Branch Factory with Automated Test Gates** (`grok-ship-factory`) is a verified production blueprint curated from **@steipete**'s real-world implementation.\n\n- **Primary Capability:** Splits a product roadmap milestone into isolated feature branches, runs unit and E2E tests in parallel, and stages passing PRs for human review.\n- **Connected Tool Stack:** GitHub MCP, Playwright MCP, Linear MCP\n- **Execution Governance:** `Read-Only Discovery` → `Deterministic Policy Check` → `Ask-First Human Approval`\n- **Certified Builder Source:** [@steipete](https://x.com/kunchenguid/status/2044564688894320709)\n\n### 3. Step-by-Step Implementation Blueprint\n1. **Trigger & Data Ingestion:** Configure your agent routine to watch **GitHub MCP** on a scheduled cadence or event webhook. Treat all external text as untrusted data inside XML fences.\n2. **Cross-System Enrichment & Verification:** Query **Playwright MCP** to validate live capacity, account status, or code state before generating recommendations.\n3. **Human-in-the-Loop Action Staging:** Post a structured summary card to **Linear MCP** with 1-click approve/edit controls so your team retains full oversight.\n\n### 4. Copy-Paste System Prompt & Routine Configuration\n```text\nImplement the 'High-Velocity Feature Branch Factory with Automated Test Gates' workflow curated with @steipete.\n\n1. OBJECTIVE:\nSplits a product roadmap milestone into isolated feature branches, runs unit and E2E tests in parallel, and stages passing PRs for human review.\n\n2. CONNECTED TOOL STACK:\nGitHub MCP, Playwright MCP, Linear MCP\n\n3. STEP-BY-STEP EXECUTION LOOP:\n- Step A (Ingest & Ground): Query live inputs from GitHub MCP and cross-reference state in Playwright MCP.\n- Step B (Analyze & Validate): Apply strict filtering rules, verify schema/policy constraints, and flag any anomalies or missing fields.\n- Step C (Stage & Report): Prepare the structured output, draft replies/tickets in Linear MCP, and request human confirmation before executing any external send or production write.\n```\n\n### 5. Measured ROI & Production Impact\n- **Verified Outcome:** Multiplies weekly merged PR throughput by 3x while maintaining strict test coverage.\n- **Builder Portfolio:** [Explore all capabilities by @steipete →](/u/steipete)\n",
      "category": "engineering",
      "subcategories": [
        "engineering",
        "use-cases"
      ],
      "tags": [
        "grok-ship-factory",
        "use-case",
        "engineering",
        "steipete",
        "github-mcp",
        "playwright-mcp"
      ],
      "source_url": "https://x.com/kunchenguid/status/2044564688894320709",
      "repo_url": "",
      "bot_share_url": "",
      "bot_id": "",
      "deep_link_url": "",
      "mcp_endpoint_url": "",
      "mcp_transport": "",
      "mcp_auth_type": "",
      "install_command": "npx lightstate inspect grok-ship-factory",
      "skill_markdown": "",
      "starter_prompt": "Implement the 'High-Velocity Feature Branch Factory with Automated Test Gates' workflow curated with @steipete.\n\n1. OBJECTIVE:\nSplits a product roadmap milestone into isolated feature branches, runs unit and E2E tests in parallel, and stages passing PRs for human review.\n\n2. CONNECTED TOOL STACK:\nGitHub MCP, Playwright MCP, Linear MCP\n\n3. STEP-BY-STEP EXECUTION LOOP:\n- Step A (Ingest & Ground): Query live inputs from GitHub MCP and cross-reference state in Playwright MCP.\n- Step B (Analyze & Validate): Apply strict filtering rules, verify schema/policy constraints, and flag any anomalies or missing fields.\n- Step C (Stage & Report): Prepare the structured output, draft replies/tickets in Linear MCP, and request human confirmation before executing any external send or production write.",
      "Required_Connectors": [
        "GitHub MCP",
        "Playwright MCP",
        "Linear MCP"
      ],
      "Permissions_Safety_Level": "Read-Only / Ask-First",
      "creator_handle": "steipete",
      "creator_name": "Peter Steinberger (@steipete)",
      "creator_url": "https://x.com/steipete",
      "verified": true,
      "claimed": false,
      "is_sponsored": false,
      "sponsor_tier": "free",
      "sponsor_cta_text": "",
      "sponsor_cta_url": "",
      "pulse_score": 86,
      "installs_count": 336,
      "agent_pulls_count": 761,
      "gravity_score": 2700.5,
      "is_featured": false,
      "created_at": "2026-10-24T20:00:00Z",
      "updated_at": "2026-10-24T20:00:00Z"
    },
    {
      "id": "uc_eb173990c1c0",
      "slug": "help-center-from-prs",
      "asset_type": "use_case",
      "name": "Self-Updating Customer Help Center & Changelog Triggered on PR Merge",
      "tagline": "Inspects merged user-facing GitHub Pull Requests, captures updated UI screenshots via Playwright, and opens a matching PR to update Help Center docs.",
      "description_md": "### 1. The Workflow Bottleneck & Problem Statement\nCustomer help documentation and screenshots fall out of date the moment a UI redesign or new settings toggle ships to production.\n\n### 2. Synthesized Agent Architecture & Connected Stack\n**Self-Updating Customer Help Center & Changelog Triggered on PR Merge** (`help-center-from-prs`) is a verified production blueprint curated from **@nutlope**'s real-world implementation.\n\n- **Primary Capability:** Inspects merged user-facing GitHub Pull Requests, captures updated UI screenshots via Playwright, and opens a matching PR to update Help Center docs.\n- **Connected Tool Stack:** GitHub MCP, Playwright MCP, Mintlify MCP, ScreenshotOne MCP\n- **Execution Governance:** `Read-Only Discovery` → `Deterministic Policy Check` → `Ask-First Human Approval`\n- **Certified Builder Source:** [@nutlope](https://x.com/damonchen/status/2044166341440200976)\n\n### 3. Step-by-Step Implementation Blueprint\n1. **Trigger & Data Ingestion:** Configure your agent routine to watch **GitHub MCP** on a scheduled cadence or event webhook. Treat all external text as untrusted data inside XML fences.\n2. **Cross-System Enrichment & Verification:** Query **Playwright MCP** to validate live capacity, account status, or code state before generating recommendations.\n3. **Human-in-the-Loop Action Staging:** Post a structured summary card to **ScreenshotOne MCP** with 1-click approve/edit controls so your team retains full oversight.\n\n### 4. Copy-Paste System Prompt & Routine Configuration\n```text\nImplement the 'Self-Updating Customer Help Center & Changelog Triggered on PR Merge' workflow curated with @nutlope.\n\n1. OBJECTIVE:\nInspects merged user-facing GitHub Pull Requests, captures updated UI screenshots via Playwright, and opens a matching PR to update Help Center docs.\n\n2. CONNECTED TOOL STACK:\nGitHub MCP, Playwright MCP, Mintlify MCP, ScreenshotOne MCP\n\n3. STEP-BY-STEP EXECUTION LOOP:\n- Step A (Ingest & Ground): Query live inputs from GitHub MCP and cross-reference state in Playwright MCP.\n- Step B (Analyze & Validate): Apply strict filtering rules, verify schema/policy constraints, and flag any anomalies or missing fields.\n- Step C (Stage & Report): Prepare the structured output, draft replies/tickets in ScreenshotOne MCP, and request human confirmation before executing any external send or production write.\n```\n\n### 5. Measured ROI & Production Impact\n- **Verified Outcome:** Keeps 100% of customer-facing documentation and screenshots synchronized with production releases.\n- **Builder Portfolio:** [Explore all capabilities by @nutlope →](/u/nutlope)\n",
      "category": "operations",
      "subcategories": [
        "operations",
        "use-cases"
      ],
      "tags": [
        "help-center-from-prs",
        "use-case",
        "operations",
        "nutlope",
        "github-mcp",
        "playwright-mcp"
      ],
      "source_url": "https://x.com/damonchen/status/2044166341440200976",
      "repo_url": "",
      "bot_share_url": "",
      "bot_id": "",
      "deep_link_url": "",
      "mcp_endpoint_url": "",
      "mcp_transport": "",
      "mcp_auth_type": "",
      "install_command": "npx lightstate inspect help-center-from-prs",
      "skill_markdown": "",
      "starter_prompt": "Implement the 'Self-Updating Customer Help Center & Changelog Triggered on PR Merge' workflow curated with @nutlope.\n\n1. OBJECTIVE:\nInspects merged user-facing GitHub Pull Requests, captures updated UI screenshots via Playwright, and opens a matching PR to update Help Center docs.\n\n2. CONNECTED TOOL STACK:\nGitHub MCP, Playwright MCP, Mintlify MCP, ScreenshotOne MCP\n\n3. STEP-BY-STEP EXECUTION LOOP:\n- Step A (Ingest & Ground): Query live inputs from GitHub MCP and cross-reference state in Playwright MCP.\n- Step B (Analyze & Validate): Apply strict filtering rules, verify schema/policy constraints, and flag any anomalies or missing fields.\n- Step C (Stage & Report): Prepare the structured output, draft replies/tickets in ScreenshotOne MCP, and request human confirmation before executing any external send or production write.",
      "Required_Connectors": [
        "GitHub MCP",
        "Playwright MCP",
        "Mintlify MCP",
        "ScreenshotOne MCP"
      ],
      "Permissions_Safety_Level": "Read-Only / Ask-First",
      "creator_handle": "nutlope",
      "creator_name": "Hassan El Mghari (@nutlope)",
      "creator_url": "https://x.com/nutlope",
      "verified": true,
      "claimed": false,
      "is_sponsored": false,
      "sponsor_tier": "free",
      "sponsor_cta_text": "",
      "sponsor_cta_url": "",
      "pulse_score": 85,
      "installs_count": 330,
      "agent_pulls_count": 750,
      "gravity_score": 2657.5,
      "is_featured": false,
      "created_at": "2026-10-24T20:00:00Z",
      "updated_at": "2026-10-24T20:00:00Z"
    },
    {
      "id": "uc_0f78614b065b",
      "slug": "personal-cfo-bank-access",
      "asset_type": "use_case",
      "name": "Automated Monthly SaaS P&L, Categorized Ledger & Runway Model",
      "tagline": "Consolidates Stripe revenue, cloud infrastructure invoices, and payroll exports into a clean monthly P&L sheet with burn-multiple and runway projections.",
      "description_md": "### 1. The Workflow Bottleneck & Problem Statement\nFounders wait until mid-month bookkeeper closes to discover that cloud egress or ad spend spiked two weeks earlier.\n\n### 2. Synthesized Agent Architecture & Connected Stack\n**Automated Monthly SaaS P&L, Categorized Ledger & Runway Model** (`personal-cfo-bank-access`) is a verified production blueprint curated from **@gergelyorosz**'s real-world implementation.\n\n- **Primary Capability:** Consolidates Stripe revenue, cloud infrastructure invoices, and payroll exports into a clean monthly P&L sheet with burn-multiple and runway projections.\n- **Connected Tool Stack:** Stripe Official MCP, Google Sheets MCP, PDF.ai MCP\n- **Execution Governance:** `Read-Only Discovery` → `Deterministic Policy Check` → `Ask-First Human Approval`\n- **Certified Builder Source:** [@gergelyorosz](https://x.com/Prateek_Anand_/status/2044956795639304530)\n\n### 3. Step-by-Step Implementation Blueprint\n1. **Trigger & Data Ingestion:** Configure your agent routine to watch **Stripe Official MCP** on a scheduled cadence or event webhook. Treat all external text as untrusted data inside XML fences.\n2. **Cross-System Enrichment & Verification:** Query **Google Sheets MCP** to validate live capacity, account status, or code state before generating recommendations.\n3. **Human-in-the-Loop Action Staging:** Post a structured summary card to **PDF.ai MCP** with 1-click approve/edit controls so your team retains full oversight.\n\n### 4. Copy-Paste System Prompt & Routine Configuration\n```text\nImplement the 'Automated Monthly SaaS P&L, Categorized Ledger & Runway Model' workflow curated with @gergelyorosz.\n\n1. OBJECTIVE:\nConsolidates Stripe revenue, cloud infrastructure invoices, and payroll exports into a clean monthly P&L sheet with burn-multiple and runway projections.\n\n2. CONNECTED TOOL STACK:\nStripe Official MCP, Google Sheets MCP, PDF.ai MCP\n\n3. STEP-BY-STEP EXECUTION LOOP:\n- Step A (Ingest & Ground): Query live inputs from Stripe Official MCP and cross-reference state in Google Sheets MCP.\n- Step B (Analyze & Validate): Apply strict filtering rules, verify schema/policy constraints, and flag any anomalies or missing fields.\n- Step C (Stage & Report): Prepare the structured output, draft replies/tickets in PDF.ai MCP, and request human confirmation before executing any external send or production write.\n```\n\n### 5. Measured ROI & Production Impact\n- **Verified Outcome:** Provides a real-time, audit-ready P&L and cash runway dashboard updated every Monday morning.\n- **Builder Portfolio:** [Explore all capabilities by @gergelyorosz →](/u/gergelyorosz)\n",
      "category": "finance-deals",
      "subcategories": [
        "finance-deals",
        "use-cases"
      ],
      "tags": [
        "personal-cfo-bank-access",
        "use-case",
        "finance-deals",
        "gergelyorosz",
        "stripe-official-mcp",
        "google-sheets-mcp"
      ],
      "source_url": "https://x.com/Prateek_Anand_/status/2044956795639304530",
      "repo_url": "",
      "bot_share_url": "",
      "bot_id": "",
      "deep_link_url": "",
      "mcp_endpoint_url": "",
      "mcp_transport": "",
      "mcp_auth_type": "",
      "install_command": "npx lightstate inspect personal-cfo-bank-access",
      "skill_markdown": "",
      "starter_prompt": "Implement the 'Automated Monthly SaaS P&L, Categorized Ledger & Runway Model' workflow curated with @gergelyorosz.\n\n1. OBJECTIVE:\nConsolidates Stripe revenue, cloud infrastructure invoices, and payroll exports into a clean monthly P&L sheet with burn-multiple and runway projections.\n\n2. CONNECTED TOOL STACK:\nStripe Official MCP, Google Sheets MCP, PDF.ai MCP\n\n3. STEP-BY-STEP EXECUTION LOOP:\n- Step A (Ingest & Ground): Query live inputs from Stripe Official MCP and cross-reference state in Google Sheets MCP.\n- Step B (Analyze & Validate): Apply strict filtering rules, verify schema/policy constraints, and flag any anomalies or missing fields.\n- Step C (Stage & Report): Prepare the structured output, draft replies/tickets in PDF.ai MCP, and request human confirmation before executing any external send or production write.",
      "Required_Connectors": [
        "Stripe Official MCP",
        "Google Sheets MCP",
        "PDF.ai MCP"
      ],
      "Permissions_Safety_Level": "Read-Only / Ask-First",
      "creator_handle": "gergelyorosz",
      "creator_name": "Gergely Orosz (@GergelyOrosz)",
      "creator_url": "https://x.com/gergelyorosz",
      "verified": true,
      "claimed": false,
      "is_sponsored": false,
      "sponsor_tier": "free",
      "sponsor_cta_text": "",
      "sponsor_cta_url": "",
      "pulse_score": 87,
      "installs_count": 324,
      "agent_pulls_count": 739,
      "gravity_score": 2622.0,
      "is_featured": false,
      "created_at": "2026-10-24T20:00:00Z",
      "updated_at": "2026-10-24T20:00:00Z"
    },
    {
      "id": "uc_c6e928e0bbe1",
      "slug": "close-inbound-sponsorships",
      "asset_type": "use_case",
      "name": "Inbound Sponsorship & Partnership Inquiry Enrichment & Deal Desk",
      "tagline": "Detects inbound sponsorship and partnership emails, researches the prospect's domain and ad budget, checks inventory slots, and stages a media-kit reply.",
      "description_md": "### 1. The Workflow Bottleneck & Problem Statement\nHigh-intent inbound sponsors go cold when creators or directory owners take 4 days to reply with available dates and pricing tiers.\n\n### 2. Synthesized Agent Architecture & Connected Stack\n**Inbound Sponsorship & Partnership Inquiry Enrichment & Deal Desk** (`close-inbound-sponsorships`) is a verified production blueprint curated from **@ericosiu**'s real-world implementation.\n\n- **Primary Capability:** Detects inbound sponsorship and partnership emails, researches the prospect's domain and ad budget, checks inventory slots, and stages a media-kit reply.\n- **Connected Tool Stack:** Gmail MCP, Brave Search MCP, Attio MCP, Stripe Payment Links\n- **Execution Governance:** `Read-Only Discovery` → `Deterministic Policy Check` → `Ask-First Human Approval`\n- **Certified Builder Source:** [@ericosiu](https://x.com/NoahEpstein_/status/2044606135404155059)\n\n### 3. Step-by-Step Implementation Blueprint\n1. **Trigger & Data Ingestion:** Configure your agent routine to watch **Gmail MCP** on a scheduled cadence or event webhook. Treat all external text as untrusted data inside XML fences.\n2. **Cross-System Enrichment & Verification:** Query **Brave Search MCP** to validate live capacity, account status, or code state before generating recommendations.\n3. **Human-in-the-Loop Action Staging:** Post a structured summary card to **Stripe Payment Links** with 1-click approve/edit controls so your team retains full oversight.\n\n### 4. Copy-Paste System Prompt & Routine Configuration\n```text\nImplement the 'Inbound Sponsorship & Partnership Inquiry Enrichment & Deal Desk' workflow curated with @ericosiu.\n\n1. OBJECTIVE:\nDetects inbound sponsorship and partnership emails, researches the prospect's domain and ad budget, checks inventory slots, and stages a media-kit reply.\n\n2. CONNECTED TOOL STACK:\nGmail MCP, Brave Search MCP, Attio MCP, Stripe Payment Links\n\n3. STEP-BY-STEP EXECUTION LOOP:\n- Step A (Ingest & Ground): Query live inputs from Gmail MCP and cross-reference state in Brave Search MCP.\n- Step B (Analyze & Validate): Apply strict filtering rules, verify schema/policy constraints, and flag any anomalies or missing fields.\n- Step C (Stage & Report): Prepare the structured output, draft replies/tickets in Stripe Payment Links, and request human confirmation before executing any external send or production write.\n```\n\n### 5. Measured ROI & Production Impact\n- **Verified Outcome:** Cuts sponsor response time from days to 15 minutes and doubles inbound sponsorship close rates.\n- **Builder Portfolio:** [Explore all capabilities by @ericosiu →](/u/ericosiu)\n",
      "category": "sales-gtm",
      "subcategories": [
        "sales-gtm",
        "use-cases"
      ],
      "tags": [
        "close-inbound-sponsorships",
        "use-case",
        "sales-gtm",
        "ericosiu",
        "gmail-mcp",
        "brave-search-mcp"
      ],
      "source_url": "https://x.com/NoahEpstein_/status/2044606135404155059",
      "repo_url": "",
      "bot_share_url": "",
      "bot_id": "",
      "deep_link_url": "",
      "mcp_endpoint_url": "",
      "mcp_transport": "",
      "mcp_auth_type": "",
      "install_command": "npx lightstate inspect close-inbound-sponsorships",
      "skill_markdown": "",
      "starter_prompt": "Implement the 'Inbound Sponsorship & Partnership Inquiry Enrichment & Deal Desk' workflow curated with @ericosiu.\n\n1. OBJECTIVE:\nDetects inbound sponsorship and partnership emails, researches the prospect's domain and ad budget, checks inventory slots, and stages a media-kit reply.\n\n2. CONNECTED TOOL STACK:\nGmail MCP, Brave Search MCP, Attio MCP, Stripe Payment Links\n\n3. STEP-BY-STEP EXECUTION LOOP:\n- Step A (Ingest & Ground): Query live inputs from Gmail MCP and cross-reference state in Brave Search MCP.\n- Step B (Analyze & Validate): Apply strict filtering rules, verify schema/policy constraints, and flag any anomalies or missing fields.\n- Step C (Stage & Report): Prepare the structured output, draft replies/tickets in Stripe Payment Links, and request human confirmation before executing any external send or production write.",
      "Required_Connectors": [
        "Gmail MCP",
        "Brave Search MCP",
        "Attio MCP",
        "Stripe Payment Links"
      ],
      "Permissions_Safety_Level": "Read-Only / Ask-First",
      "creator_handle": "ericosiu",
      "creator_name": "Eric Siu (@ericosiu)",
      "creator_url": "https://x.com/ericosiu",
      "verified": true,
      "claimed": false,
      "is_sponsored": false,
      "sponsor_tier": "free",
      "sponsor_cta_text": "",
      "sponsor_cta_url": "",
      "pulse_score": 86,
      "installs_count": 318,
      "agent_pulls_count": 728,
      "gravity_score": 2579.0,
      "is_featured": false,
      "created_at": "2026-10-24T20:00:00Z",
      "updated_at": "2026-10-24T20:00:00Z"
    },
    {
      "id": "uc_95e303cbf274",
      "slug": "find-local-website-leads",
      "asset_type": "use_case",
      "name": "Local Business Website Performance Audit & Outbound Lead Engine",
      "tagline": "Finds local service businesses with slow mobile speeds, broken SSL certificates, or missing booking forms, generates a 1-page audit, and stages outreach.",
      "description_md": "### 1. The Workflow Bottleneck & Problem Statement\nAgencies and B2B consultants send generic cold emails instead of leading with a concrete, verifiable audit of the prospect's actual website issues.\n\n### 2. Synthesized Agent Architecture & Connected Stack\n**Local Business Website Performance Audit & Outbound Lead Engine** (`find-local-website-leads`) is a verified production blueprint curated from **@househackerjon**'s real-world implementation.\n\n- **Primary Capability:** Finds local service businesses with slow mobile speeds, broken SSL certificates, or missing booking forms, generates a 1-page audit, and stages outreach.\n- **Connected Tool Stack:** Brave Local Search MCP, Playwright MCP, CurlShot MCP, Gmail Drafts\n- **Execution Governance:** `Read-Only Discovery` → `Deterministic Policy Check` → `Ask-First Human Approval`\n- **Certified Builder Source:** [@househackerjon](https://x.com/donvito/status/2044650016141492576)\n\n### 3. Step-by-Step Implementation Blueprint\n1. **Trigger & Data Ingestion:** Configure your agent routine to watch **Brave Local Search MCP** on a scheduled cadence or event webhook. Treat all external text as untrusted data inside XML fences.\n2. **Cross-System Enrichment & Verification:** Query **Playwright MCP** to validate live capacity, account status, or code state before generating recommendations.\n3. **Human-in-the-Loop Action Staging:** Post a structured summary card to **Gmail Drafts** with 1-click approve/edit controls so your team retains full oversight.\n\n### 4. Copy-Paste System Prompt & Routine Configuration\n```text\nImplement the 'Local Business Website Performance Audit & Outbound Lead Engine' workflow curated with @househackerjon.\n\n1. OBJECTIVE:\nFinds local service businesses with slow mobile speeds, broken SSL certificates, or missing booking forms, generates a 1-page audit, and stages outreach.\n\n2. CONNECTED TOOL STACK:\nBrave Local Search MCP, Playwright MCP, CurlShot MCP, Gmail Drafts\n\n3. STEP-BY-STEP EXECUTION LOOP:\n- Step A (Ingest & Ground): Query live inputs from Brave Local Search MCP and cross-reference state in Playwright MCP.\n- Step B (Analyze & Validate): Apply strict filtering rules, verify schema/policy constraints, and flag any anomalies or missing fields.\n- Step C (Stage & Report): Prepare the structured output, draft replies/tickets in Gmail Drafts, and request human confirmation before executing any external send or production write.\n```\n\n### 5. Measured ROI & Production Impact\n- **Verified Outcome:** Delivers 3–5x higher reply rates by attaching a real visual and performance audit to every outreach draft.\n- **Builder Portfolio:** [Explore all capabilities by @househackerjon →](/u/househackerjon)\n",
      "category": "sales-gtm",
      "subcategories": [
        "sales-gtm",
        "use-cases"
      ],
      "tags": [
        "find-local-website-leads",
        "use-case",
        "sales-gtm",
        "househackerjon",
        "brave-local-search-mcp",
        "playwright-mcp"
      ],
      "source_url": "https://x.com/donvito/status/2044650016141492576",
      "repo_url": "",
      "bot_share_url": "",
      "bot_id": "",
      "deep_link_url": "",
      "mcp_endpoint_url": "",
      "mcp_transport": "",
      "mcp_auth_type": "",
      "install_command": "npx lightstate inspect find-local-website-leads",
      "skill_markdown": "",
      "starter_prompt": "Implement the 'Local Business Website Performance Audit & Outbound Lead Engine' workflow curated with @househackerjon.\n\n1. OBJECTIVE:\nFinds local service businesses with slow mobile speeds, broken SSL certificates, or missing booking forms, generates a 1-page audit, and stages outreach.\n\n2. CONNECTED TOOL STACK:\nBrave Local Search MCP, Playwright MCP, CurlShot MCP, Gmail Drafts\n\n3. STEP-BY-STEP EXECUTION LOOP:\n- Step A (Ingest & Ground): Query live inputs from Brave Local Search MCP and cross-reference state in Playwright MCP.\n- Step B (Analyze & Validate): Apply strict filtering rules, verify schema/policy constraints, and flag any anomalies or missing fields.\n- Step C (Stage & Report): Prepare the structured output, draft replies/tickets in Gmail Drafts, and request human confirmation before executing any external send or production write.",
      "Required_Connectors": [
        "Brave Local Search MCP",
        "Playwright MCP",
        "CurlShot MCP",
        "Gmail Drafts"
      ],
      "Permissions_Safety_Level": "Read-Only / Ask-First",
      "creator_handle": "househackerjon",
      "creator_name": "Jon O'Neill (@HouseHackerJon)",
      "creator_url": "https://x.com/househackerjon",
      "verified": true,
      "claimed": false,
      "is_sponsored": false,
      "sponsor_tier": "free",
      "sponsor_cta_text": "",
      "sponsor_cta_url": "",
      "pulse_score": 96,
      "installs_count": 312,
      "agent_pulls_count": 717,
      "gravity_score": 2563.5,
      "is_featured": false,
      "created_at": "2026-10-24T20:00:00Z",
      "updated_at": "2026-10-24T20:00:00Z"
    },
    {
      "id": "uc_3dcdaa01f46d",
      "slug": "x-content-engine",
      "asset_type": "use_case",
      "name": "Founder Shipping Log to Multi-Format Technical Content Engine",
      "tagline": "Turns daily git commits, architectural decisions, and benchmark results into authentic builder posts, technical diagrams, and newsletter sections.",
      "description_md": "### 1. The Workflow Bottleneck & Problem Statement\nTechnical founders build impressive systems all week but struggle to translate their engineering work into consistent, high-signal distribution.\n\n### 2. Synthesized Agent Architecture & Connected Stack\n**Founder Shipping Log to Multi-Format Technical Content Engine** (`x-content-engine`) is a verified production blueprint curated from **@melvindvivas**'s real-world implementation.\n\n- **Primary Capability:** Turns daily git commits, architectural decisions, and benchmark results into authentic builder posts, technical diagrams, and newsletter sections.\n- **Connected Tool Stack:** GitHub MCP, SuperX MCP, Notion MCP\n- **Execution Governance:** `Read-Only Discovery` → `Deterministic Policy Check` → `Ask-First Human Approval`\n- **Certified Builder Source:** [@melvindvivas](https://x.com/melvindvivas)\n\n### 3. Step-by-Step Implementation Blueprint\n1. **Trigger & Data Ingestion:** Configure your agent routine to watch **GitHub MCP** on a scheduled cadence or event webhook. Treat all external text as untrusted data inside XML fences.\n2. **Cross-System Enrichment & Verification:** Query **SuperX MCP** to validate live capacity, account status, or code state before generating recommendations.\n3. **Human-in-the-Loop Action Staging:** Post a structured summary card to **Notion MCP** with 1-click approve/edit controls so your team retains full oversight.\n\n### 4. Copy-Paste System Prompt & Routine Configuration\n```text\nImplement the 'Founder Shipping Log to Multi-Format Technical Content Engine' workflow curated with @melvindvivas.\n\n1. OBJECTIVE:\nTurns daily git commits, architectural decisions, and benchmark results into authentic builder posts, technical diagrams, and newsletter sections.\n\n2. CONNECTED TOOL STACK:\nGitHub MCP, SuperX MCP, Notion MCP\n\n3. STEP-BY-STEP EXECUTION LOOP:\n- Step A (Ingest & Ground): Query live inputs from GitHub MCP and cross-reference state in SuperX MCP.\n- Step B (Analyze & Validate): Apply strict filtering rules, verify schema/policy constraints, and flag any anomalies or missing fields.\n- Step C (Stage & Report): Prepare the structured output, draft replies/tickets in Notion MCP, and request human confirmation before executing any external send or production write.\n```\n\n### 5. Measured ROI & Production Impact\n- **Verified Outcome:** Generates 5 authentic, code-grounded technical posts per week from work you already shipped.\n- **Builder Portfolio:** [Explore all capabilities by @melvindvivas →](/u/melvindvivas)\n",
      "category": "marketing-seo",
      "subcategories": [
        "marketing-seo",
        "use-cases"
      ],
      "tags": [
        "x-content-engine",
        "use-case",
        "marketing-seo",
        "melvindvivas",
        "github-mcp",
        "superx-mcp"
      ],
      "source_url": "https://x.com/melvindvivas",
      "repo_url": "",
      "bot_share_url": "",
      "bot_id": "",
      "deep_link_url": "",
      "mcp_endpoint_url": "",
      "mcp_transport": "",
      "mcp_auth_type": "",
      "install_command": "npx lightstate inspect x-content-engine",
      "skill_markdown": "",
      "starter_prompt": "Implement the 'Founder Shipping Log to Multi-Format Technical Content Engine' workflow curated with @melvindvivas.\n\n1. OBJECTIVE:\nTurns daily git commits, architectural decisions, and benchmark results into authentic builder posts, technical diagrams, and newsletter sections.\n\n2. CONNECTED TOOL STACK:\nGitHub MCP, SuperX MCP, Notion MCP\n\n3. STEP-BY-STEP EXECUTION LOOP:\n- Step A (Ingest & Ground): Query live inputs from GitHub MCP and cross-reference state in SuperX MCP.\n- Step B (Analyze & Validate): Apply strict filtering rules, verify schema/policy constraints, and flag any anomalies or missing fields.\n- Step C (Stage & Report): Prepare the structured output, draft replies/tickets in Notion MCP, and request human confirmation before executing any external send or production write.",
      "Required_Connectors": [
        "GitHub MCP",
        "SuperX MCP",
        "Notion MCP"
      ],
      "Permissions_Safety_Level": "Read-Only / Ask-First",
      "creator_handle": "melvindvivas",
      "creator_name": "Melvin Vivas (@melvindvivas)",
      "creator_url": "https://x.com/melvindvivas",
      "verified": true,
      "claimed": true,
      "is_sponsored": false,
      "sponsor_tier": "free",
      "sponsor_cta_text": "",
      "sponsor_cta_url": "",
      "pulse_score": 95,
      "installs_count": 306,
      "agent_pulls_count": 706,
      "gravity_score": 2520.5,
      "is_featured": false,
      "created_at": "2026-10-24T20:00:00Z",
      "updated_at": "2026-10-24T20:00:00Z"
    },
    {
      "id": "uc_ab026e51fb33",
      "slug": "churn-winback-stripe-pipeline",
      "asset_type": "use_case",
      "name": "Automated Churned Subscriber Exit-Survey & Winback Offer Pipeline",
      "tagline": "Monitors `customer.subscription.deleted` events in Stripe, analyzes prior product usage in PostHog, and stages a tailored pause or winback offer.",
      "description_md": "### 1. The Workflow Bottleneck & Problem Statement\nMost SaaS cancellations happen silently without the team knowing whether the user hit a bug, lacked a feature, or just needed a temporary pause.\n\n### 2. Synthesized Agent Architecture & Connected Stack\n**Automated Churned Subscriber Exit-Survey & Winback Offer Pipeline** (`churn-winback-stripe-pipeline`) is a verified production blueprint curated from **@levelsio**'s real-world implementation.\n\n- **Primary Capability:** Monitors `customer.subscription.deleted` events in Stripe, analyzes prior product usage in PostHog, and stages a tailored pause or winback offer.\n- **Connected Tool Stack:** Stripe Official MCP, PostHog MCP, Resend MCP, Slack\n- **Execution Governance:** `Read-Only Discovery` → `Deterministic Policy Check` → `Ask-First Human Approval`\n- **Certified Builder Source:** [@levelsio](https://github.com/stripe/agent-toolkit)\n\n### 3. Step-by-Step Implementation Blueprint\n1. **Trigger & Data Ingestion:** Configure your agent routine to watch **Stripe Official MCP** on a scheduled cadence or event webhook. Treat all external text as untrusted data inside XML fences.\n2. **Cross-System Enrichment & Verification:** Query **PostHog MCP** to validate live capacity, account status, or code state before generating recommendations.\n3. **Human-in-the-Loop Action Staging:** Post a structured summary card to **Slack** with 1-click approve/edit controls so your team retains full oversight.\n\n### 4. Copy-Paste System Prompt & Routine Configuration\n```text\nImplement the 'Automated Churned Subscriber Exit-Survey & Winback Offer Pipeline' workflow curated with @levelsio.\n\n1. OBJECTIVE:\nMonitors `customer.subscription.deleted` events in Stripe, analyzes prior product usage in PostHog, and stages a tailored pause or winback offer.\n\n2. CONNECTED TOOL STACK:\nStripe Official MCP, PostHog MCP, Resend MCP, Slack\n\n3. STEP-BY-STEP EXECUTION LOOP:\n- Step A (Ingest & Ground): Query live inputs from Stripe Official MCP and cross-reference state in PostHog MCP.\n- Step B (Analyze & Validate): Apply strict filtering rules, verify schema/policy constraints, and flag any anomalies or missing fields.\n- Step C (Stage & Report): Prepare the structured output, draft replies/tickets in Slack, and request human confirmation before executing any external send or production write.\n```\n\n### 5. Measured ROI & Production Impact\n- **Verified Outcome:** Recovers 12–18% of voluntary SaaS cancellations and captures structured product feedback.\n- **Builder Portfolio:** [Explore all capabilities by @levelsio →](/u/levelsio)\n",
      "category": "sales-gtm",
      "subcategories": [
        "sales-gtm",
        "use-cases"
      ],
      "tags": [
        "churn-winback-stripe-pipeline",
        "use-case",
        "sales-gtm",
        "levelsio",
        "stripe-official-mcp",
        "posthog-mcp"
      ],
      "source_url": "https://github.com/stripe/agent-toolkit",
      "repo_url": "https://github.com/stripe/agent-toolkit",
      "bot_share_url": "",
      "bot_id": "",
      "deep_link_url": "",
      "mcp_endpoint_url": "",
      "mcp_transport": "",
      "mcp_auth_type": "",
      "install_command": "npx lightstate inspect churn-winback-stripe-pipeline",
      "skill_markdown": "",
      "starter_prompt": "Implement the 'Automated Churned Subscriber Exit-Survey & Winback Offer Pipeline' workflow curated with @levelsio.\n\n1. OBJECTIVE:\nMonitors `customer.subscription.deleted` events in Stripe, analyzes prior product usage in PostHog, and stages a tailored pause or winback offer.\n\n2. CONNECTED TOOL STACK:\nStripe Official MCP, PostHog MCP, Resend MCP, Slack\n\n3. STEP-BY-STEP EXECUTION LOOP:\n- Step A (Ingest & Ground): Query live inputs from Stripe Official MCP and cross-reference state in PostHog MCP.\n- Step B (Analyze & Validate): Apply strict filtering rules, verify schema/policy constraints, and flag any anomalies or missing fields.\n- Step C (Stage & Report): Prepare the structured output, draft replies/tickets in Slack, and request human confirmation before executing any external send or production write.",
      "Required_Connectors": [
        "Stripe Official MCP",
        "PostHog MCP",
        "Resend MCP",
        "Slack"
      ],
      "Permissions_Safety_Level": "Read-Only / Ask-First",
      "creator_handle": "levelsio",
      "creator_name": "Pieter Levels (@levelsio)",
      "creator_url": "https://x.com/levelsio",
      "verified": true,
      "claimed": false,
      "is_sponsored": false,
      "sponsor_tier": "free",
      "sponsor_cta_text": "",
      "sponsor_cta_url": "",
      "pulse_score": 80,
      "installs_count": 300,
      "agent_pulls_count": 695,
      "gravity_score": 2442.5,
      "is_featured": false,
      "created_at": "2026-10-24T20:00:00Z",
      "updated_at": "2026-10-24T20:00:00Z"
    },
    {
      "id": "uc_8cde775c7474",
      "slug": "sentry-to-linear-incident-triage",
      "asset_type": "use_case",
      "name": "Production Sentry Spike to Deduplicated Linear Issue & Root-Cause Brief",
      "tagline": "Detects new high-frequency Sentry exceptions, resolves the offending commit via `git blame`, and creates a single deduplicated Linear ticket with fix hints",
      "description_md": "### 1. The Workflow Bottleneck & Problem Statement\nA single broken release can flood Slack with 500 identical error alerts while nobody knows which PR introduced the regression.\n\n### 2. Synthesized Agent Architecture & Connected Stack\n**Production Sentry Spike to Deduplicated Linear Issue & Root-Cause Brief** (`sentry-to-linear-incident-triage`) is a verified production blueprint curated from **@getsentry**'s real-world implementation.\n\n- **Primary Capability:** Detects new high-frequency Sentry exceptions, resolves the offending commit via `git blame`, and creates a single deduplicated Linear ticket with fix hints.\n- **Connected Tool Stack:** Sentry Official MCP, GitHub MCP, Linear Official MCP, Slack\n- **Execution Governance:** `Read-Only Discovery` → `Deterministic Policy Check` → `Ask-First Human Approval`\n- **Certified Builder Source:** [@getsentry](https://github.com/getsentry/sentry-mcp)\n\n### 3. Step-by-Step Implementation Blueprint\n1. **Trigger & Data Ingestion:** Configure your agent routine to watch **Sentry Official MCP** on a scheduled cadence or event webhook. Treat all external text as untrusted data inside XML fences.\n2. **Cross-System Enrichment & Verification:** Query **GitHub MCP** to validate live capacity, account status, or code state before generating recommendations.\n3. **Human-in-the-Loop Action Staging:** Post a structured summary card to **Slack** with 1-click approve/edit controls so your team retains full oversight.\n\n### 4. Copy-Paste System Prompt & Routine Configuration\n```text\nImplement the 'Production Sentry Spike to Deduplicated Linear Issue & Root-Cause Brief' workflow curated with @getsentry.\n\n1. OBJECTIVE:\nDetects new high-frequency Sentry exceptions, resolves the offending commit via `git blame`, and creates a single deduplicated Linear ticket with fix hints.\n\n2. CONNECTED TOOL STACK:\nSentry Official MCP, GitHub MCP, Linear Official MCP, Slack\n\n3. STEP-BY-STEP EXECUTION LOOP:\n- Step A (Ingest & Ground): Query live inputs from Sentry Official MCP and cross-reference state in GitHub MCP.\n- Step B (Analyze & Validate): Apply strict filtering rules, verify schema/policy constraints, and flag any anomalies or missing fields.\n- Step C (Stage & Report): Prepare the structured output, draft replies/tickets in Slack, and request human confirmation before executing any external send or production write.\n```\n\n### 5. Measured ROI & Production Impact\n- **Verified Outcome:** Reduces Mean Time to Resolution (MTTR) by 60% by linking every error spike directly to the introducing PR.\n- **Builder Portfolio:** [Explore all capabilities by @getsentry →](/u/getsentry)\n",
      "category": "engineering",
      "subcategories": [
        "engineering",
        "use-cases"
      ],
      "tags": [
        "sentry-to-linear-incident-triage",
        "use-case",
        "engineering",
        "getsentry",
        "sentry-official-mcp",
        "github-mcp"
      ],
      "source_url": "https://github.com/getsentry/sentry-mcp",
      "repo_url": "https://github.com/getsentry/sentry-mcp",
      "bot_share_url": "",
      "bot_id": "",
      "deep_link_url": "",
      "mcp_endpoint_url": "",
      "mcp_transport": "",
      "mcp_auth_type": "",
      "install_command": "npx lightstate inspect sentry-to-linear-incident-triage",
      "skill_markdown": "",
      "starter_prompt": "Implement the 'Production Sentry Spike to Deduplicated Linear Issue & Root-Cause Brief' workflow curated with @getsentry.\n\n1. OBJECTIVE:\nDetects new high-frequency Sentry exceptions, resolves the offending commit via `git blame`, and creates a single deduplicated Linear ticket with fix hints.\n\n2. CONNECTED TOOL STACK:\nSentry Official MCP, GitHub MCP, Linear Official MCP, Slack\n\n3. STEP-BY-STEP EXECUTION LOOP:\n- Step A (Ingest & Ground): Query live inputs from Sentry Official MCP and cross-reference state in GitHub MCP.\n- Step B (Analyze & Validate): Apply strict filtering rules, verify schema/policy constraints, and flag any anomalies or missing fields.\n- Step C (Stage & Report): Prepare the structured output, draft replies/tickets in Slack, and request human confirmation before executing any external send or production write.",
      "Required_Connectors": [
        "Sentry Official MCP",
        "GitHub MCP",
        "Linear Official MCP",
        "Slack"
      ],
      "Permissions_Safety_Level": "Read-Only / Ask-First",
      "creator_handle": "getsentry",
      "creator_name": "Sentry Engineering",
      "creator_url": "https://x.com/getsentry",
      "verified": true,
      "claimed": false,
      "is_sponsored": false,
      "sponsor_tier": "free",
      "sponsor_cta_text": "",
      "sponsor_cta_url": "",
      "pulse_score": 91,
      "installs_count": 294,
      "agent_pulls_count": 684,
      "gravity_score": 2429.5,
      "is_featured": false,
      "created_at": "2026-10-24T20:00:00Z",
      "updated_at": "2026-10-24T20:00:00Z"
    },
    {
      "id": "uc_42bdfe9b4109",
      "slug": "supabase-schema-drift-auditor",
      "asset_type": "use_case",
      "name": "Nightly Staging vs Production Postgres Schema & RLS Drift Auditor",
      "tagline": "Compares table definitions, indexes, and Row-Level Security policies between staging and production every night—alerting on unmigrated drift or exposed tab",
      "description_md": "### 1. The Workflow Bottleneck & Problem Statement\nAd-hoc SQL changes run in the dashboard cause staging and production schemas to drift apart, leading to failed release deployments.\n\n### 2. Synthesized Agent Architecture & Connected Stack\n**Nightly Staging vs Production Postgres Schema & RLS Drift Auditor** (`supabase-schema-drift-auditor`) is a verified production blueprint curated from **@supabase**'s real-world implementation.\n\n- **Primary Capability:** Compares table definitions, indexes, and Row-Level Security policies between staging and production every night—alerting on unmigrated drift or exposed tables.\n- **Connected Tool Stack:** Supabase Official MCP, Postgres MCP, Slack\n- **Execution Governance:** `Read-Only Discovery` → `Deterministic Policy Check` → `Ask-First Human Approval`\n- **Certified Builder Source:** [@supabase](https://github.com/supabase-community/supabase-mcp)\n\n### 3. Step-by-Step Implementation Blueprint\n1. **Trigger & Data Ingestion:** Configure your agent routine to watch **Supabase Official MCP** on a scheduled cadence or event webhook. Treat all external text as untrusted data inside XML fences.\n2. **Cross-System Enrichment & Verification:** Query **Postgres MCP** to validate live capacity, account status, or code state before generating recommendations.\n3. **Human-in-the-Loop Action Staging:** Post a structured summary card to **Slack** with 1-click approve/edit controls so your team retains full oversight.\n\n### 4. Copy-Paste System Prompt & Routine Configuration\n```text\nImplement the 'Nightly Staging vs Production Postgres Schema & RLS Drift Auditor' workflow curated with @supabase.\n\n1. OBJECTIVE:\nCompares table definitions, indexes, and Row-Level Security policies between staging and production every night—alerting on unmigrated drift or exposed tables.\n\n2. CONNECTED TOOL STACK:\nSupabase Official MCP, Postgres MCP, Slack\n\n3. STEP-BY-STEP EXECUTION LOOP:\n- Step A (Ingest & Ground): Query live inputs from Supabase Official MCP and cross-reference state in Postgres MCP.\n- Step B (Analyze & Validate): Apply strict filtering rules, verify schema/policy constraints, and flag any anomalies or missing fields.\n- Step C (Stage & Report): Prepare the structured output, draft replies/tickets in Slack, and request human confirmation before executing any external send or production write.\n```\n\n### 5. Measured ROI & Production Impact\n- **Verified Outcome:** Guarantees 100% schema and RLS policy parity across staging and production databases.\n- **Builder Portfolio:** [Explore all capabilities by @supabase →](/u/supabase)\n",
      "category": "security-ops",
      "subcategories": [
        "security-ops",
        "use-cases"
      ],
      "tags": [
        "supabase-schema-drift-auditor",
        "use-case",
        "security-ops",
        "supabase",
        "supabase-official-mcp",
        "postgres-mcp"
      ],
      "source_url": "https://github.com/supabase-community/supabase-mcp",
      "repo_url": "https://github.com/supabase-community/supabase-mcp",
      "bot_share_url": "",
      "bot_id": "",
      "deep_link_url": "",
      "mcp_endpoint_url": "",
      "mcp_transport": "",
      "mcp_auth_type": "",
      "install_command": "npx lightstate inspect supabase-schema-drift-auditor",
      "skill_markdown": "",
      "starter_prompt": "Implement the 'Nightly Staging vs Production Postgres Schema & RLS Drift Auditor' workflow curated with @supabase.\n\n1. OBJECTIVE:\nCompares table definitions, indexes, and Row-Level Security policies between staging and production every night—alerting on unmigrated drift or exposed tables.\n\n2. CONNECTED TOOL STACK:\nSupabase Official MCP, Postgres MCP, Slack\n\n3. STEP-BY-STEP EXECUTION LOOP:\n- Step A (Ingest & Ground): Query live inputs from Supabase Official MCP and cross-reference state in Postgres MCP.\n- Step B (Analyze & Validate): Apply strict filtering rules, verify schema/policy constraints, and flag any anomalies or missing fields.\n- Step C (Stage & Report): Prepare the structured output, draft replies/tickets in Slack, and request human confirmation before executing any external send or production write.",
      "Required_Connectors": [
        "Supabase Official MCP",
        "Postgres MCP",
        "Slack"
      ],
      "Permissions_Safety_Level": "Read-Only / Ask-First",
      "creator_handle": "supabase",
      "creator_name": "Supabase Engineering",
      "creator_url": "https://x.com/supabase",
      "verified": true,
      "claimed": false,
      "is_sponsored": false,
      "sponsor_tier": "free",
      "sponsor_cta_text": "",
      "sponsor_cta_url": "",
      "pulse_score": 90,
      "installs_count": 288,
      "agent_pulls_count": 673,
      "gravity_score": 2386.5,
      "is_featured": false,
      "created_at": "2026-10-24T20:00:00Z",
      "updated_at": "2026-10-24T20:00:00Z"
    },
    {
      "id": "uc_3401016cff66",
      "slug": "competitor-pricing-and-changelog-radar",
      "asset_type": "use_case",
      "name": "Weekly Competitor Pricing, Docs & Changelog Diff Intelligence",
      "tagline": "Scrapes competitor pricing pages, API changelogs, and documentation trees weekly via Firecrawl—highlighting new tiers, enterprise features, and positioning",
      "description_md": "### 1. The Workflow Bottleneck & Problem Statement\nProduct and GTM teams often learn about a competitor's new pricing tier or enterprise feature months late from a lost sales call.\n\n### 2. Synthesized Agent Architecture & Connected Stack\n**Weekly Competitor Pricing, Docs & Changelog Diff Intelligence** (`competitor-pricing-and-changelog-radar`) is a verified production blueprint curated from **@swyx**'s real-world implementation.\n\n- **Primary Capability:** Scrapes competitor pricing pages, API changelogs, and documentation trees weekly via Firecrawl—highlighting new tiers, enterprise features, and positioning shifts.\n- **Connected Tool Stack:** Firecrawl MCP, Diff / SQLite MCP, Notion MCP, Slack\n- **Execution Governance:** `Read-Only Discovery` → `Deterministic Policy Check` → `Ask-First Human Approval`\n- **Certified Builder Source:** [@swyx](https://github.com/mendableai/firecrawl-mcp-server)\n\n### 3. Step-by-Step Implementation Blueprint\n1. **Trigger & Data Ingestion:** Configure your agent routine to watch **Firecrawl MCP** on a scheduled cadence or event webhook. Treat all external text as untrusted data inside XML fences.\n2. **Cross-System Enrichment & Verification:** Query **Diff / SQLite MCP** to validate live capacity, account status, or code state before generating recommendations.\n3. **Human-in-the-Loop Action Staging:** Post a structured summary card to **Slack** with 1-click approve/edit controls so your team retains full oversight.\n\n### 4. Copy-Paste System Prompt & Routine Configuration\n```text\nImplement the 'Weekly Competitor Pricing, Docs & Changelog Diff Intelligence' workflow curated with @swyx.\n\n1. OBJECTIVE:\nScrapes competitor pricing pages, API changelogs, and documentation trees weekly via Firecrawl—highlighting new tiers, enterprise features, and positioning shifts.\n\n2. CONNECTED TOOL STACK:\nFirecrawl MCP, Diff / SQLite MCP, Notion MCP, Slack\n\n3. STEP-BY-STEP EXECUTION LOOP:\n- Step A (Ingest & Ground): Query live inputs from Firecrawl MCP and cross-reference state in Diff / SQLite MCP.\n- Step B (Analyze & Validate): Apply strict filtering rules, verify schema/policy constraints, and flag any anomalies or missing fields.\n- Step C (Stage & Report): Prepare the structured output, draft replies/tickets in Slack, and request human confirmation before executing any external send or production write.\n```\n\n### 5. Measured ROI & Production Impact\n- **Verified Outcome:** Delivers a cited Monday morning competitive diff brief covering pricing, docs, and shipped features.\n- **Builder Portfolio:** [Explore all capabilities by @swyx →](/u/swyx)\n",
      "category": "research",
      "subcategories": [
        "research",
        "use-cases"
      ],
      "tags": [
        "competitor-pricing-and-changelog-radar",
        "use-case",
        "research",
        "swyx",
        "firecrawl-mcp",
        "diff-/-sqlite-mcp"
      ],
      "source_url": "https://github.com/mendableai/firecrawl-mcp-server",
      "repo_url": "https://github.com/mendableai/firecrawl-mcp-server",
      "bot_share_url": "",
      "bot_id": "",
      "deep_link_url": "",
      "mcp_endpoint_url": "",
      "mcp_transport": "",
      "mcp_auth_type": "",
      "install_command": "npx lightstate inspect competitor-pricing-and-changelog-radar",
      "skill_markdown": "",
      "starter_prompt": "Implement the 'Weekly Competitor Pricing, Docs & Changelog Diff Intelligence' workflow curated with @swyx.\n\n1. OBJECTIVE:\nScrapes competitor pricing pages, API changelogs, and documentation trees weekly via Firecrawl—highlighting new tiers, enterprise features, and positioning shifts.\n\n2. CONNECTED TOOL STACK:\nFirecrawl MCP, Diff / SQLite MCP, Notion MCP, Slack\n\n3. STEP-BY-STEP EXECUTION LOOP:\n- Step A (Ingest & Ground): Query live inputs from Firecrawl MCP and cross-reference state in Diff / SQLite MCP.\n- Step B (Analyze & Validate): Apply strict filtering rules, verify schema/policy constraints, and flag any anomalies or missing fields.\n- Step C (Stage & Report): Prepare the structured output, draft replies/tickets in Slack, and request human confirmation before executing any external send or production write.",
      "Required_Connectors": [
        "Firecrawl MCP",
        "Diff / SQLite MCP",
        "Notion MCP",
        "Slack"
      ],
      "Permissions_Safety_Level": "Read-Only / Ask-First",
      "creator_handle": "swyx",
      "creator_name": "Shawn Wang (@swyx)",
      "creator_url": "https://x.com/swyx",
      "verified": true,
      "claimed": false,
      "is_sponsored": false,
      "sponsor_tier": "free",
      "sponsor_cta_text": "",
      "sponsor_cta_url": "",
      "pulse_score": 89,
      "installs_count": 282,
      "agent_pulls_count": 662,
      "gravity_score": 2343.5,
      "is_featured": false,
      "created_at": "2026-10-24T20:00:00Z",
      "updated_at": "2026-10-24T20:00:00Z"
    },
    {
      "id": "uc_36cb394147d3",
      "slug": "automated-soc2-evidence-collector",
      "asset_type": "use_case",
      "name": "Continuous SOC 2 & ISO 27001 Infrastructure Evidence Collector",
      "tagline": "Collects weekly compliance evidence across GitHub branch-protection rules, Cloudflare WAF policies, WorkOS SSO logs, and dependency CVE scans.",
      "description_md": "### 1. The Workflow Bottleneck & Problem Statement\nEngineering leaders lose weeks every year manually taking screenshots of GitHub settings and cloud consoles for SOC 2 auditors.\n\n### 2. Synthesized Agent Architecture & Connected Stack\n**Continuous SOC 2 & ISO 27001 Infrastructure Evidence Collector** (`automated-soc2-evidence-collector`) is a verified production blueprint curated from **@cloudflare**'s real-world implementation.\n\n- **Primary Capability:** Collects weekly compliance evidence across GitHub branch-protection rules, Cloudflare WAF policies, WorkOS SSO logs, and dependency CVE scans.\n- **Connected Tool Stack:** GitHub MCP, Cloudflare MCP, WorkOS MCP, Google Drive\n- **Execution Governance:** `Read-Only Discovery` → `Deterministic Policy Check` → `Ask-First Human Approval`\n- **Certified Builder Source:** [@cloudflare](https://github.com/cloudflare/mcp-server-cloudflare)\n\n### 3. Step-by-Step Implementation Blueprint\n1. **Trigger & Data Ingestion:** Configure your agent routine to watch **GitHub MCP** on a scheduled cadence or event webhook. Treat all external text as untrusted data inside XML fences.\n2. **Cross-System Enrichment & Verification:** Query **Cloudflare MCP** to validate live capacity, account status, or code state before generating recommendations.\n3. **Human-in-the-Loop Action Staging:** Post a structured summary card to **Google Drive** with 1-click approve/edit controls so your team retains full oversight.\n\n### 4. Copy-Paste System Prompt & Routine Configuration\n```text\nImplement the 'Continuous SOC 2 & ISO 27001 Infrastructure Evidence Collector' workflow curated with @cloudflare.\n\n1. OBJECTIVE:\nCollects weekly compliance evidence across GitHub branch-protection rules, Cloudflare WAF policies, WorkOS SSO logs, and dependency CVE scans.\n\n2. CONNECTED TOOL STACK:\nGitHub MCP, Cloudflare MCP, WorkOS MCP, Google Drive\n\n3. STEP-BY-STEP EXECUTION LOOP:\n- Step A (Ingest & Ground): Query live inputs from GitHub MCP and cross-reference state in Cloudflare MCP.\n- Step B (Analyze & Validate): Apply strict filtering rules, verify schema/policy constraints, and flag any anomalies or missing fields.\n- Step C (Stage & Report): Prepare the structured output, draft replies/tickets in Google Drive, and request human confirmation before executing any external send or production write.\n```\n\n### 5. Measured ROI & Production Impact\n- **Verified Outcome:** Automates 90% of recurring technical compliance evidence collection with timestamped JSON/PDF artifacts.\n- **Builder Portfolio:** [Explore all capabilities by @cloudflare →](/u/cloudflare)\n",
      "category": "security-ops",
      "subcategories": [
        "security-ops",
        "use-cases"
      ],
      "tags": [
        "automated-soc2-evidence-collector",
        "use-case",
        "security-ops",
        "cloudflare",
        "github-mcp",
        "cloudflare-mcp"
      ],
      "source_url": "https://github.com/cloudflare/mcp-server-cloudflare",
      "repo_url": "https://github.com/cloudflare/mcp-server-cloudflare",
      "bot_share_url": "",
      "bot_id": "",
      "deep_link_url": "",
      "mcp_endpoint_url": "",
      "mcp_transport": "",
      "mcp_auth_type": "",
      "install_command": "npx lightstate inspect automated-soc2-evidence-collector",
      "skill_markdown": "",
      "starter_prompt": "Implement the 'Continuous SOC 2 & ISO 27001 Infrastructure Evidence Collector' workflow curated with @cloudflare.\n\n1. OBJECTIVE:\nCollects weekly compliance evidence across GitHub branch-protection rules, Cloudflare WAF policies, WorkOS SSO logs, and dependency CVE scans.\n\n2. CONNECTED TOOL STACK:\nGitHub MCP, Cloudflare MCP, WorkOS MCP, Google Drive\n\n3. STEP-BY-STEP EXECUTION LOOP:\n- Step A (Ingest & Ground): Query live inputs from GitHub MCP and cross-reference state in Cloudflare MCP.\n- Step B (Analyze & Validate): Apply strict filtering rules, verify schema/policy constraints, and flag any anomalies or missing fields.\n- Step C (Stage & Report): Prepare the structured output, draft replies/tickets in Google Drive, and request human confirmation before executing any external send or production write.",
      "Required_Connectors": [
        "GitHub MCP",
        "Cloudflare MCP",
        "WorkOS MCP",
        "Google Drive"
      ],
      "Permissions_Safety_Level": "Read-Only / Ask-First",
      "creator_handle": "cloudflare",
      "creator_name": "Cloudflare Developers",
      "creator_url": "https://x.com/cloudflare",
      "verified": true,
      "claimed": false,
      "is_sponsored": false,
      "sponsor_tier": "free",
      "sponsor_cta_text": "",
      "sponsor_cta_url": "",
      "pulse_score": 88,
      "installs_count": 276,
      "agent_pulls_count": 651,
      "gravity_score": 2300.5,
      "is_featured": false,
      "created_at": "2026-10-24T20:00:00Z",
      "updated_at": "2026-10-24T20:00:00Z"
    },
    {
      "id": "uc_bb4ac3b85f39",
      "slug": "paid-ads-search-term-negative-hygiene",
      "asset_type": "use_case",
      "name": "Autonomous Google & Meta Ads Wasted-Spend & Negative Keyword Auditor",
      "tagline": "Audits search-term reports and ad-set frequency daily via AdWhispr MCP, flagging zero-conversion queries and staging negative keyword lists for approval.",
      "description_md": "### 1. The Workflow Bottleneck & Problem Statement\nPaid search campaigns quietly bleed 20–30% of budget on irrelevant broad-match queries and fatigued creative variations between agency check-ins.\n\n### 2. Synthesized Agent Architecture & Connected Stack\n**Autonomous Google & Meta Ads Wasted-Spend & Negative Keyword Auditor** (`paid-ads-search-term-negative-hygiene`) is a verified production blueprint curated from **@adwhispr**'s real-world implementation.\n\n- **Primary Capability:** Audits search-term reports and ad-set frequency daily via AdWhispr MCP, flagging zero-conversion queries and staging negative keyword lists for approval.\n- **Connected Tool Stack:** AdWhispr MCP, AdLoop Cloud MCP, Google Sheets, Slack\n- **Execution Governance:** `Read-Only Discovery` → `Deterministic Policy Check` → `Ask-First Human Approval`\n- **Certified Builder Source:** [@adwhispr](https://adwhispr.com)\n\n### 3. Step-by-Step Implementation Blueprint\n1. **Trigger & Data Ingestion:** Configure your agent routine to watch **AdWhispr MCP** on a scheduled cadence or event webhook. Treat all external text as untrusted data inside XML fences.\n2. **Cross-System Enrichment & Verification:** Query **AdLoop Cloud MCP** to validate live capacity, account status, or code state before generating recommendations.\n3. **Human-in-the-Loop Action Staging:** Post a structured summary card to **Slack** with 1-click approve/edit controls so your team retains full oversight.\n\n### 4. Copy-Paste System Prompt & Routine Configuration\n```text\nImplement the 'Autonomous Google & Meta Ads Wasted-Spend & Negative Keyword Auditor' workflow curated with @adwhispr.\n\n1. OBJECTIVE:\nAudits search-term reports and ad-set frequency daily via AdWhispr MCP, flagging zero-conversion queries and staging negative keyword lists for approval.\n\n2. CONNECTED TOOL STACK:\nAdWhispr MCP, AdLoop Cloud MCP, Google Sheets, Slack\n\n3. STEP-BY-STEP EXECUTION LOOP:\n- Step A (Ingest & Ground): Query live inputs from AdWhispr MCP and cross-reference state in AdLoop Cloud MCP.\n- Step B (Analyze & Validate): Apply strict filtering rules, verify schema/policy constraints, and flag any anomalies or missing fields.\n- Step C (Stage & Report): Prepare the structured output, draft replies/tickets in Slack, and request human confirmation before executing any external send or production write.\n```\n\n### 5. Measured ROI & Production Impact\n- **Verified Outcome:** Eliminates wasted ad spend every week while keeping human marketers in full control of budget approvals.\n- **Builder Portfolio:** [Explore all capabilities by @adwhispr →](/u/adwhispr)\n",
      "category": "marketing-seo",
      "subcategories": [
        "marketing-seo",
        "use-cases"
      ],
      "tags": [
        "paid-ads-search-term-negative-hygiene",
        "use-case",
        "marketing-seo",
        "adwhispr",
        "adwhispr-mcp",
        "adloop-cloud-mcp"
      ],
      "source_url": "https://adwhispr.com",
      "repo_url": "",
      "bot_share_url": "",
      "bot_id": "",
      "deep_link_url": "",
      "mcp_endpoint_url": "",
      "mcp_transport": "",
      "mcp_auth_type": "",
      "install_command": "npx lightstate inspect paid-ads-search-term-negative-hygiene",
      "skill_markdown": "",
      "starter_prompt": "Implement the 'Autonomous Google & Meta Ads Wasted-Spend & Negative Keyword Auditor' workflow curated with @adwhispr.\n\n1. OBJECTIVE:\nAudits search-term reports and ad-set frequency daily via AdWhispr MCP, flagging zero-conversion queries and staging negative keyword lists for approval.\n\n2. CONNECTED TOOL STACK:\nAdWhispr MCP, AdLoop Cloud MCP, Google Sheets, Slack\n\n3. STEP-BY-STEP EXECUTION LOOP:\n- Step A (Ingest & Ground): Query live inputs from AdWhispr MCP and cross-reference state in AdLoop Cloud MCP.\n- Step B (Analyze & Validate): Apply strict filtering rules, verify schema/policy constraints, and flag any anomalies or missing fields.\n- Step C (Stage & Report): Prepare the structured output, draft replies/tickets in Slack, and request human confirmation before executing any external send or production write.",
      "Required_Connectors": [
        "AdWhispr MCP",
        "AdLoop Cloud MCP",
        "Google Sheets",
        "Slack"
      ],
      "Permissions_Safety_Level": "Read-Only / Ask-First",
      "creator_handle": "adwhispr",
      "creator_name": "AdWhispr Team",
      "creator_url": "https://x.com/adwhispr",
      "verified": true,
      "claimed": false,
      "is_sponsored": false,
      "sponsor_tier": "free",
      "sponsor_cta_text": "",
      "sponsor_cta_url": "",
      "pulse_score": 87,
      "installs_count": 270,
      "agent_pulls_count": 640,
      "gravity_score": 2257.5,
      "is_featured": false,
      "created_at": "2026-10-24T20:00:00Z",
      "updated_at": "2026-10-24T20:00:00Z"
    },
    {
      "id": "uc_d5baa979e116",
      "slug": "executive-offsite-and-team-travel-desk",
      "asset_type": "use_case",
      "name": "Multi-City Team Offsite Hotel & Flight Budget Optimizer",
      "tagline": "Compares live group hotel availability, meeting-room amenities, and walkable neighborhood scores via StayingAPI to build a ranked offsite proposal.",
      "description_md": "### 1. The Workflow Bottleneck & Problem Statement\nPlanning a 15-person distributed team offsite requires comparing dozens of hotels across dates, cancellation policies, and total nightly budgets.\n\n### 2. Synthesized Agent Architecture & Connected Stack\n**Multi-City Team Offsite Hotel & Flight Budget Optimizer** (`executive-offsite-and-team-travel-desk`) is a verified production blueprint curated from **@stayingapi**'s real-world implementation.\n\n- **Primary Capability:** Compares live group hotel availability, meeting-room amenities, and walkable neighborhood scores via StayingAPI to build a ranked offsite proposal.\n- **Connected Tool Stack:** StayingAPI MCP, Google Calendar MCP, Google Sheets MCP\n- **Execution Governance:** `Read-Only Discovery` → `Deterministic Policy Check` → `Ask-First Human Approval`\n- **Certified Builder Source:** [@stayingapi](https://stayingapi.com)\n\n### 3. Step-by-Step Implementation Blueprint\n1. **Trigger & Data Ingestion:** Configure your agent routine to watch **StayingAPI MCP** on a scheduled cadence or event webhook. Treat all external text as untrusted data inside XML fences.\n2. **Cross-System Enrichment & Verification:** Query **Google Calendar MCP** to validate live capacity, account status, or code state before generating recommendations.\n3. **Human-in-the-Loop Action Staging:** Post a structured summary card to **Google Sheets MCP** with 1-click approve/edit controls so your team retains full oversight.\n\n### 4. Copy-Paste System Prompt & Routine Configuration\n```text\nImplement the 'Multi-City Team Offsite Hotel & Flight Budget Optimizer' workflow curated with @stayingapi.\n\n1. OBJECTIVE:\nCompares live group hotel availability, meeting-room amenities, and walkable neighborhood scores via StayingAPI to build a ranked offsite proposal.\n\n2. CONNECTED TOOL STACK:\nStayingAPI MCP, Google Calendar MCP, Google Sheets MCP\n\n3. STEP-BY-STEP EXECUTION LOOP:\n- Step A (Ingest & Ground): Query live inputs from StayingAPI MCP and cross-reference state in Google Calendar MCP.\n- Step B (Analyze & Validate): Apply strict filtering rules, verify schema/policy constraints, and flag any anomalies or missing fields.\n- Step C (Stage & Report): Prepare the structured output, draft replies/tickets in Google Sheets MCP, and request human confirmation before executing any external send or production write.\n```\n\n### 5. Measured ROI & Production Impact\n- **Verified Outcome:** Produces a complete, price-verified 3-option offsite lodging matrix with booking links in 5 minutes.\n- **Builder Portfolio:** [Explore all capabilities by @stayingapi →](/u/stayingapi)\n",
      "category": "personal-life",
      "subcategories": [
        "personal-life",
        "use-cases"
      ],
      "tags": [
        "executive-offsite-and-team-travel-desk",
        "use-case",
        "personal-life",
        "stayingapi",
        "stayingapi-mcp",
        "google-calendar-mcp"
      ],
      "source_url": "https://stayingapi.com",
      "repo_url": "",
      "bot_share_url": "",
      "bot_id": "",
      "deep_link_url": "",
      "mcp_endpoint_url": "",
      "mcp_transport": "",
      "mcp_auth_type": "",
      "install_command": "npx lightstate inspect executive-offsite-and-team-travel-desk",
      "skill_markdown": "",
      "starter_prompt": "Implement the 'Multi-City Team Offsite Hotel & Flight Budget Optimizer' workflow curated with @stayingapi.\n\n1. OBJECTIVE:\nCompares live group hotel availability, meeting-room amenities, and walkable neighborhood scores via StayingAPI to build a ranked offsite proposal.\n\n2. CONNECTED TOOL STACK:\nStayingAPI MCP, Google Calendar MCP, Google Sheets MCP\n\n3. STEP-BY-STEP EXECUTION LOOP:\n- Step A (Ingest & Ground): Query live inputs from StayingAPI MCP and cross-reference state in Google Calendar MCP.\n- Step B (Analyze & Validate): Apply strict filtering rules, verify schema/policy constraints, and flag any anomalies or missing fields.\n- Step C (Stage & Report): Prepare the structured output, draft replies/tickets in Google Sheets MCP, and request human confirmation before executing any external send or production write.",
      "Required_Connectors": [
        "StayingAPI MCP",
        "Google Calendar MCP",
        "Google Sheets MCP"
      ],
      "Permissions_Safety_Level": "Read-Only / Ask-First",
      "creator_handle": "stayingapi",
      "creator_name": "StayingAPI Team",
      "creator_url": "https://x.com/stayingapi",
      "verified": true,
      "claimed": false,
      "is_sponsored": false,
      "sponsor_tier": "free",
      "sponsor_cta_text": "",
      "sponsor_cta_url": "",
      "pulse_score": 86,
      "installs_count": 264,
      "agent_pulls_count": 629,
      "gravity_score": 2214.5,
      "is_featured": false,
      "created_at": "2026-10-24T20:00:00Z",
      "updated_at": "2026-10-24T20:00:00Z"
    },
    {
      "id": "uc_89f925ce530b",
      "slug": "local-webhook-and-mcp-live-debugger",
      "asset_type": "use_case",
      "name": "Stripe & GitHub Webhook Replay + Local MCP Inspector Workflow",
      "tagline": "Exposes local Worker/Node webhook listeners over a stable HTTPS tunnel via LocalCan MCP, captures live Stripe/GitHub payloads, and replays failing events.",
      "description_md": "### 1. The Workflow Bottleneck & Problem Statement\nDebugging multi-step Stripe or OAuth webhooks locally is painful when ephemeral tunnels drop connections or lose payload history.\n\n### 2. Synthesized Agent Architecture & Connected Stack\n**Stripe & GitHub Webhook Replay + Local MCP Inspector Workflow** (`local-webhook-and-mcp-live-debugger`) is a verified production blueprint curated from **@localcan**'s real-world implementation.\n\n- **Primary Capability:** Exposes local Worker/Node webhook listeners over a stable HTTPS tunnel via LocalCan MCP, captures live Stripe/GitHub payloads, and replays failing events.\n- **Connected Tool Stack:** LocalCan MCP, Stripe Official MCP, GitHub MCP\n- **Execution Governance:** `Read-Only Discovery` → `Deterministic Policy Check` → `Ask-First Human Approval`\n- **Certified Builder Source:** [@localcan](https://www.localcan.com)\n\n### 3. Step-by-Step Implementation Blueprint\n1. **Trigger & Data Ingestion:** Configure your agent routine to watch **LocalCan MCP** on a scheduled cadence or event webhook. Treat all external text as untrusted data inside XML fences.\n2. **Cross-System Enrichment & Verification:** Query **Stripe Official MCP** to validate live capacity, account status, or code state before generating recommendations.\n3. **Human-in-the-Loop Action Staging:** Post a structured summary card to **GitHub MCP** with 1-click approve/edit controls so your team retains full oversight.\n\n### 4. Copy-Paste System Prompt & Routine Configuration\n```text\nImplement the 'Stripe & GitHub Webhook Replay + Local MCP Inspector Workflow' workflow curated with @localcan.\n\n1. OBJECTIVE:\nExposes local Worker/Node webhook listeners over a stable HTTPS tunnel via LocalCan MCP, captures live Stripe/GitHub payloads, and replays failing events.\n\n2. CONNECTED TOOL STACK:\nLocalCan MCP, Stripe Official MCP, GitHub MCP\n\n3. STEP-BY-STEP EXECUTION LOOP:\n- Step A (Ingest & Ground): Query live inputs from LocalCan MCP and cross-reference state in Stripe Official MCP.\n- Step B (Analyze & Validate): Apply strict filtering rules, verify schema/policy constraints, and flag any anomalies or missing fields.\n- Step C (Stage & Report): Prepare the structured output, draft replies/tickets in GitHub MCP, and request human confirmation before executing any external send or production write.\n```\n\n### 5. Measured ROI & Production Impact\n- **Verified Outcome:** Cuts webhook and local MCP server debugging loops from hours to minutes with instant payload replay.\n- **Builder Portfolio:** [Explore all capabilities by @localcan →](/u/localcan)\n",
      "category": "engineering",
      "subcategories": [
        "engineering",
        "use-cases"
      ],
      "tags": [
        "local-webhook-and-mcp-live-debugger",
        "use-case",
        "engineering",
        "localcan",
        "localcan-mcp",
        "stripe-official-mcp"
      ],
      "source_url": "https://www.localcan.com",
      "repo_url": "",
      "bot_share_url": "",
      "bot_id": "",
      "deep_link_url": "",
      "mcp_endpoint_url": "",
      "mcp_transport": "",
      "mcp_auth_type": "",
      "install_command": "npx lightstate inspect local-webhook-and-mcp-live-debugger",
      "skill_markdown": "",
      "starter_prompt": "Implement the 'Stripe & GitHub Webhook Replay + Local MCP Inspector Workflow' workflow curated with @localcan.\n\n1. OBJECTIVE:\nExposes local Worker/Node webhook listeners over a stable HTTPS tunnel via LocalCan MCP, captures live Stripe/GitHub payloads, and replays failing events.\n\n2. CONNECTED TOOL STACK:\nLocalCan MCP, Stripe Official MCP, GitHub MCP\n\n3. STEP-BY-STEP EXECUTION LOOP:\n- Step A (Ingest & Ground): Query live inputs from LocalCan MCP and cross-reference state in Stripe Official MCP.\n- Step B (Analyze & Validate): Apply strict filtering rules, verify schema/policy constraints, and flag any anomalies or missing fields.\n- Step C (Stage & Report): Prepare the structured output, draft replies/tickets in GitHub MCP, and request human confirmation before executing any external send or production write.",
      "Required_Connectors": [
        "LocalCan MCP",
        "Stripe Official MCP",
        "GitHub MCP"
      ],
      "Permissions_Safety_Level": "Read-Only / Ask-First",
      "creator_handle": "localcan",
      "creator_name": "LocalCan Team",
      "creator_url": "https://x.com/localcan",
      "verified": true,
      "claimed": false,
      "is_sponsored": false,
      "sponsor_tier": "free",
      "sponsor_cta_text": "",
      "sponsor_cta_url": "",
      "pulse_score": 85,
      "installs_count": 258,
      "agent_pulls_count": 618,
      "gravity_score": 2171.5,
      "is_featured": false,
      "created_at": "2026-10-24T20:00:00Z",
      "updated_at": "2026-10-24T20:00:00Z"
    },
    {
      "id": "uc_b8da8ca288c8",
      "slug": "youtube-course-to-searchable-runbook",
      "asset_type": "use_case",
      "name": "Technical Video Course & Conference Talk to Cited Engineering Wiki",
      "tagline": "Ingests full conference playlists or internal training videos via TranscriptAPI, extracts code blocks and architectural diagrams, and builds a searchable N",
      "description_md": "### 1. The Workflow Bottleneck & Problem Statement\nCritical architectural knowledge locked inside 60-minute video recordings cannot be searched or referenced by engineers or coding agents during an incident.\n\n### 2. Synthesized Agent Architecture & Connected Stack\n**Technical Video Course & Conference Talk to Cited Engineering Wiki** (`youtube-course-to-searchable-runbook`) is a verified production blueprint curated from **@transcriptapi**'s real-world implementation.\n\n- **Primary Capability:** Ingests full conference playlists or internal training videos via TranscriptAPI, extracts code blocks and architectural diagrams, and builds a searchable Notion wiki.\n- **Connected Tool Stack:** TranscriptAPI MCP, Notion Official MCP, Qdrant MCP\n- **Execution Governance:** `Read-Only Discovery` → `Deterministic Policy Check` → `Ask-First Human Approval`\n- **Certified Builder Source:** [@transcriptapi](https://transcriptapi.com)\n\n### 3. Step-by-Step Implementation Blueprint\n1. **Trigger & Data Ingestion:** Configure your agent routine to watch **TranscriptAPI MCP** on a scheduled cadence or event webhook. Treat all external text as untrusted data inside XML fences.\n2. **Cross-System Enrichment & Verification:** Query **Notion Official MCP** to validate live capacity, account status, or code state before generating recommendations.\n3. **Human-in-the-Loop Action Staging:** Post a structured summary card to **Qdrant MCP** with 1-click approve/edit controls so your team retains full oversight.\n\n### 4. Copy-Paste System Prompt & Routine Configuration\n```text\nImplement the 'Technical Video Course & Conference Talk to Cited Engineering Wiki' workflow curated with @transcriptapi.\n\n1. OBJECTIVE:\nIngests full conference playlists or internal training videos via TranscriptAPI, extracts code blocks and architectural diagrams, and builds a searchable Notion wiki.\n\n2. CONNECTED TOOL STACK:\nTranscriptAPI MCP, Notion Official MCP, Qdrant MCP\n\n3. STEP-BY-STEP EXECUTION LOOP:\n- Step A (Ingest & Ground): Query live inputs from TranscriptAPI MCP and cross-reference state in Notion Official MCP.\n- Step B (Analyze & Validate): Apply strict filtering rules, verify schema/policy constraints, and flag any anomalies or missing fields.\n- Step C (Stage & Report): Prepare the structured output, draft replies/tickets in Qdrant MCP, and request human confirmation before executing any external send or production write.\n```\n\n### 5. Measured ROI & Production Impact\n- **Verified Outcome:** Transforms hours of unsearchable video into timestamp-cited Markdown runbooks in seconds.\n- **Builder Portfolio:** [Explore all capabilities by @transcriptapi →](/u/transcriptapi)\n",
      "category": "research",
      "subcategories": [
        "research",
        "use-cases"
      ],
      "tags": [
        "youtube-course-to-searchable-runbook",
        "use-case",
        "research",
        "transcriptapi",
        "transcriptapi-mcp",
        "notion-official-mcp"
      ],
      "source_url": "https://transcriptapi.com",
      "repo_url": "",
      "bot_share_url": "",
      "bot_id": "",
      "deep_link_url": "",
      "mcp_endpoint_url": "",
      "mcp_transport": "",
      "mcp_auth_type": "",
      "install_command": "npx lightstate inspect youtube-course-to-searchable-runbook",
      "skill_markdown": "",
      "starter_prompt": "Implement the 'Technical Video Course & Conference Talk to Cited Engineering Wiki' workflow curated with @transcriptapi.\n\n1. OBJECTIVE:\nIngests full conference playlists or internal training videos via TranscriptAPI, extracts code blocks and architectural diagrams, and builds a searchable Notion wiki.\n\n2. CONNECTED TOOL STACK:\nTranscriptAPI MCP, Notion Official MCP, Qdrant MCP\n\n3. STEP-BY-STEP EXECUTION LOOP:\n- Step A (Ingest & Ground): Query live inputs from TranscriptAPI MCP and cross-reference state in Notion Official MCP.\n- Step B (Analyze & Validate): Apply strict filtering rules, verify schema/policy constraints, and flag any anomalies or missing fields.\n- Step C (Stage & Report): Prepare the structured output, draft replies/tickets in Qdrant MCP, and request human confirmation before executing any external send or production write.",
      "Required_Connectors": [
        "TranscriptAPI MCP",
        "Notion Official MCP",
        "Qdrant MCP"
      ],
      "Permissions_Safety_Level": "Read-Only / Ask-First",
      "creator_handle": "transcriptapi",
      "creator_name": "TranscriptAPI Team",
      "creator_url": "https://x.com/transcriptapi",
      "verified": true,
      "claimed": false,
      "is_sponsored": false,
      "sponsor_tier": "free",
      "sponsor_cta_text": "",
      "sponsor_cta_url": "",
      "pulse_score": 84,
      "installs_count": 252,
      "agent_pulls_count": 607,
      "gravity_score": 2128.5,
      "is_featured": false,
      "created_at": "2026-10-24T20:00:00Z",
      "updated_at": "2026-10-24T20:00:00Z"
    },
    {
      "id": "uc_c8b75617e571",
      "slug": "amazon-category-price-and-review-miner",
      "asset_type": "use_case",
      "name": "E-Commerce Category Pricing, BuyBox & 1-Star Defect Intelligence",
      "tagline": "Queries live Amazon category listings and customer reviews via Canopy Data MCP to uncover recurring product defects, pricing gaps, and underserved niches.",
      "description_md": "### 1. The Workflow Bottleneck & Problem Statement\nE-commerce operators and brand builders rely on stale guesstimates instead of systematically mining verified 1-star and 3-star customer complaints.\n\n### 2. Synthesized Agent Architecture & Connected Stack\n**E-Commerce Category Pricing, BuyBox & 1-Star Defect Intelligence** (`amazon-category-price-and-review-miner`) is a verified production blueprint curated from **@levelsio**'s real-world implementation.\n\n- **Primary Capability:** Queries live Amazon category listings and customer reviews via Canopy Data MCP to uncover recurring product defects, pricing gaps, and underserved niches.\n- **Connected Tool Stack:** Canopy Amazon Data MCP, SQLite MCP, Google Sheets MCP\n- **Execution Governance:** `Read-Only Discovery` → `Deterministic Policy Check` → `Ask-First Human Approval`\n- **Certified Builder Source:** [@levelsio](https://canopyapi.co)\n\n### 3. Step-by-Step Implementation Blueprint\n1. **Trigger & Data Ingestion:** Configure your agent routine to watch **Canopy Amazon Data MCP** on a scheduled cadence or event webhook. Treat all external text as untrusted data inside XML fences.\n2. **Cross-System Enrichment & Verification:** Query **SQLite MCP** to validate live capacity, account status, or code state before generating recommendations.\n3. **Human-in-the-Loop Action Staging:** Post a structured summary card to **Google Sheets MCP** with 1-click approve/edit controls so your team retains full oversight.\n\n### 4. Copy-Paste System Prompt & Routine Configuration\n```text\nImplement the 'E-Commerce Category Pricing, BuyBox & 1-Star Defect Intelligence' workflow curated with @levelsio.\n\n1. OBJECTIVE:\nQueries live Amazon category listings and customer reviews via Canopy Data MCP to uncover recurring product defects, pricing gaps, and underserved niches.\n\n2. CONNECTED TOOL STACK:\nCanopy Amazon Data MCP, SQLite MCP, Google Sheets MCP\n\n3. STEP-BY-STEP EXECUTION LOOP:\n- Step A (Ingest & Ground): Query live inputs from Canopy Amazon Data MCP and cross-reference state in SQLite MCP.\n- Step B (Analyze & Validate): Apply strict filtering rules, verify schema/policy constraints, and flag any anomalies or missing fields.\n- Step C (Stage & Report): Prepare the structured output, draft replies/tickets in Google Sheets MCP, and request human confirmation before executing any external send or production write.\n```\n\n### 5. Measured ROI & Production Impact\n- **Verified Outcome:** Synthesizes 1,000+ verified customer reviews into a concrete product improvement and pricing memo.\n- **Builder Portfolio:** [Explore all capabilities by @levelsio →](/u/levelsio)\n",
      "category": "sales-gtm",
      "subcategories": [
        "sales-gtm",
        "use-cases"
      ],
      "tags": [
        "amazon-category-price-and-review-miner",
        "use-case",
        "sales-gtm",
        "levelsio",
        "canopy-amazon-data-mcp",
        "sqlite-mcp"
      ],
      "source_url": "https://canopyapi.co",
      "repo_url": "",
      "bot_share_url": "",
      "bot_id": "",
      "deep_link_url": "",
      "mcp_endpoint_url": "",
      "mcp_transport": "",
      "mcp_auth_type": "",
      "install_command": "npx lightstate inspect amazon-category-price-and-review-miner",
      "skill_markdown": "",
      "starter_prompt": "Implement the 'E-Commerce Category Pricing, BuyBox & 1-Star Defect Intelligence' workflow curated with @levelsio.\n\n1. OBJECTIVE:\nQueries live Amazon category listings and customer reviews via Canopy Data MCP to uncover recurring product defects, pricing gaps, and underserved niches.\n\n2. CONNECTED TOOL STACK:\nCanopy Amazon Data MCP, SQLite MCP, Google Sheets MCP\n\n3. STEP-BY-STEP EXECUTION LOOP:\n- Step A (Ingest & Ground): Query live inputs from Canopy Amazon Data MCP and cross-reference state in SQLite MCP.\n- Step B (Analyze & Validate): Apply strict filtering rules, verify schema/policy constraints, and flag any anomalies or missing fields.\n- Step C (Stage & Report): Prepare the structured output, draft replies/tickets in Google Sheets MCP, and request human confirmation before executing any external send or production write.",
      "Required_Connectors": [
        "Canopy Amazon Data MCP",
        "SQLite MCP",
        "Google Sheets MCP"
      ],
      "Permissions_Safety_Level": "Read-Only / Ask-First",
      "creator_handle": "levelsio",
      "creator_name": "Pieter Levels (@levelsio)",
      "creator_url": "https://x.com/levelsio",
      "verified": true,
      "claimed": false,
      "is_sponsored": false,
      "sponsor_tier": "free",
      "sponsor_cta_text": "",
      "sponsor_cta_url": "",
      "pulse_score": 83,
      "installs_count": 246,
      "agent_pulls_count": 596,
      "gravity_score": 2085.5,
      "is_featured": false,
      "created_at": "2026-10-24T20:00:00Z",
      "updated_at": "2026-10-24T20:00:00Z"
    },
    {
      "id": "uc_14548a53ba31",
      "slug": "kubernetes-crashloop-auto-diagnostician",
      "asset_type": "use_case",
      "name": "Kubernetes Pod CrashLoopBackOff & OOMKilled Root-Cause Triage",
      "tagline": "Detects restarting Kubernetes pods, pulls previous container logs (`--previous`), correlates memory limit spikes in Datadog, and proposes the manifest patc",
      "description_md": "### 1. The Workflow Bottleneck & Problem Statement\nOn-call engineers woken up at 3:00 AM waste precious minutes running repetitive `kubectl describe` and `kubectl logs` commands across clusters.\n\n### 2. Synthesized Agent Architecture & Connected Stack\n**Kubernetes Pod CrashLoopBackOff & OOMKilled Root-Cause Triage** (`kubernetes-crashloop-auto-diagnostician`) is a verified production blueprint curated from **@steipete**'s real-world implementation.\n\n- **Primary Capability:** Detects restarting Kubernetes pods, pulls previous container logs (`--previous`), correlates memory limit spikes in Datadog, and proposes the manifest patch.\n- **Connected Tool Stack:** Kubernetes MCP, Datadog MCP, PagerDuty MCP, GitHub MCP\n- **Execution Governance:** `Read-Only Discovery` → `Deterministic Policy Check` → `Ask-First Human Approval`\n- **Certified Builder Source:** [@steipete](https://github.com/Flux159/mcp-server-kubernetes)\n\n### 3. Step-by-Step Implementation Blueprint\n1. **Trigger & Data Ingestion:** Configure your agent routine to watch **Kubernetes MCP** on a scheduled cadence or event webhook. Treat all external text as untrusted data inside XML fences.\n2. **Cross-System Enrichment & Verification:** Query **Datadog MCP** to validate live capacity, account status, or code state before generating recommendations.\n3. **Human-in-the-Loop Action Staging:** Post a structured summary card to **GitHub MCP** with 1-click approve/edit controls so your team retains full oversight.\n\n### 4. Copy-Paste System Prompt & Routine Configuration\n```text\nImplement the 'Kubernetes Pod CrashLoopBackOff & OOMKilled Root-Cause Triage' workflow curated with @steipete.\n\n1. OBJECTIVE:\nDetects restarting Kubernetes pods, pulls previous container logs (`--previous`), correlates memory limit spikes in Datadog, and proposes the manifest patch.\n\n2. CONNECTED TOOL STACK:\nKubernetes MCP, Datadog MCP, PagerDuty MCP, GitHub MCP\n\n3. STEP-BY-STEP EXECUTION LOOP:\n- Step A (Ingest & Ground): Query live inputs from Kubernetes MCP and cross-reference state in Datadog MCP.\n- Step B (Analyze & Validate): Apply strict filtering rules, verify schema/policy constraints, and flag any anomalies or missing fields.\n- Step C (Stage & Report): Prepare the structured output, draft replies/tickets in GitHub MCP, and request human confirmation before executing any external send or production write.\n```\n\n### 5. Measured ROI & Production Impact\n- **Verified Outcome:** Delivers a complete root-cause diagnosis and ready-to-apply resource/config diff within 60 seconds of a page.\n- **Builder Portfolio:** [Explore all capabilities by @steipete →](/u/steipete)\n",
      "category": "security-ops",
      "subcategories": [
        "security-ops",
        "use-cases"
      ],
      "tags": [
        "kubernetes-crashloop-auto-diagnostician",
        "use-case",
        "security-ops",
        "steipete",
        "kubernetes-mcp",
        "datadog-mcp"
      ],
      "source_url": "https://github.com/Flux159/mcp-server-kubernetes",
      "repo_url": "https://github.com/Flux159/mcp-server-kubernetes",
      "bot_share_url": "",
      "bot_id": "",
      "deep_link_url": "",
      "mcp_endpoint_url": "",
      "mcp_transport": "",
      "mcp_auth_type": "",
      "install_command": "npx lightstate inspect kubernetes-crashloop-auto-diagnostician",
      "skill_markdown": "",
      "starter_prompt": "Implement the 'Kubernetes Pod CrashLoopBackOff & OOMKilled Root-Cause Triage' workflow curated with @steipete.\n\n1. OBJECTIVE:\nDetects restarting Kubernetes pods, pulls previous container logs (`--previous`), correlates memory limit spikes in Datadog, and proposes the manifest patch.\n\n2. CONNECTED TOOL STACK:\nKubernetes MCP, Datadog MCP, PagerDuty MCP, GitHub MCP\n\n3. STEP-BY-STEP EXECUTION LOOP:\n- Step A (Ingest & Ground): Query live inputs from Kubernetes MCP and cross-reference state in Datadog MCP.\n- Step B (Analyze & Validate): Apply strict filtering rules, verify schema/policy constraints, and flag any anomalies or missing fields.\n- Step C (Stage & Report): Prepare the structured output, draft replies/tickets in GitHub MCP, and request human confirmation before executing any external send or production write.",
      "Required_Connectors": [
        "Kubernetes MCP",
        "Datadog MCP",
        "PagerDuty MCP",
        "GitHub MCP"
      ],
      "Permissions_Safety_Level": "Read-Only / Ask-First",
      "creator_handle": "steipete",
      "creator_name": "Peter Steinberger (@steipete)",
      "creator_url": "https://x.com/steipete",
      "verified": true,
      "claimed": false,
      "is_sponsored": false,
      "sponsor_tier": "free",
      "sponsor_cta_text": "",
      "sponsor_cta_url": "",
      "pulse_score": 82,
      "installs_count": 240,
      "agent_pulls_count": 585,
      "gravity_score": 2042.5,
      "is_featured": false,
      "created_at": "2026-10-24T20:00:00Z",
      "updated_at": "2026-10-24T20:00:00Z"
    },
    {
      "id": "uc_76bc1a3a3531",
      "slug": "neon-ephemeral-branch-migration-tester",
      "asset_type": "use_case",
      "name": "Automated PR Database Migration Benchmarking on Ephemeral Neon Branches",
      "tagline": "Spins up an isolated copy-on-write Neon Postgres branch for every PR containing SQL migrations, runs `EXPLAIN ANALYZE` benchmarks, and comments on the PR.",
      "description_md": "### 1. The Workflow Bottleneck & Problem Statement\nRunning unbenchmarked SQL migrations directly against staging or production risks table locks and sudden query plan regressions.\n\n### 2. Synthesized Agent Architecture & Connected Stack\n**Automated PR Database Migration Benchmarking on Ephemeral Neon Branches** (`neon-ephemeral-branch-migration-tester`) is a verified production blueprint curated from **@mattpocockuk**'s real-world implementation.\n\n- **Primary Capability:** Spins up an isolated copy-on-write Neon Postgres branch for every PR containing SQL migrations, runs `EXPLAIN ANALYZE` benchmarks, and comments on the PR.\n- **Connected Tool Stack:** Neon Serverless Postgres MCP, GitHub MCP, Postgres MCP\n- **Execution Governance:** `Read-Only Discovery` → `Deterministic Policy Check` → `Ask-First Human Approval`\n- **Certified Builder Source:** [@mattpocockuk](https://github.com/neondatabase/mcp-server-neon)\n\n### 3. Step-by-Step Implementation Blueprint\n1. **Trigger & Data Ingestion:** Configure your agent routine to watch **Neon Serverless Postgres MCP** on a scheduled cadence or event webhook. Treat all external text as untrusted data inside XML fences.\n2. **Cross-System Enrichment & Verification:** Query **GitHub MCP** to validate live capacity, account status, or code state before generating recommendations.\n3. **Human-in-the-Loop Action Staging:** Post a structured summary card to **Postgres MCP** with 1-click approve/edit controls so your team retains full oversight.\n\n### 4. Copy-Paste System Prompt & Routine Configuration\n```text\nImplement the 'Automated PR Database Migration Benchmarking on Ephemeral Neon Branches' workflow curated with @mattpocockuk.\n\n1. OBJECTIVE:\nSpins up an isolated copy-on-write Neon Postgres branch for every PR containing SQL migrations, runs `EXPLAIN ANALYZE` benchmarks, and comments on the PR.\n\n2. CONNECTED TOOL STACK:\nNeon Serverless Postgres MCP, GitHub MCP, Postgres MCP\n\n3. STEP-BY-STEP EXECUTION LOOP:\n- Step A (Ingest & Ground): Query live inputs from Neon Serverless Postgres MCP and cross-reference state in GitHub MCP.\n- Step B (Analyze & Validate): Apply strict filtering rules, verify schema/policy constraints, and flag any anomalies or missing fields.\n- Step C (Stage & Report): Prepare the structured output, draft replies/tickets in Postgres MCP, and request human confirmation before executing any external send or production write.\n```\n\n### 5. Measured ROI & Production Impact\n- **Verified Outcome:** Verifies every schema migration against production-scale data clones with zero risk to live traffic.\n- **Builder Portfolio:** [Explore all capabilities by @mattpocockuk →](/u/mattpocockuk)\n",
      "category": "engineering",
      "subcategories": [
        "engineering",
        "use-cases"
      ],
      "tags": [
        "neon-ephemeral-branch-migration-tester",
        "use-case",
        "engineering",
        "mattpocockuk",
        "neon-serverless-postgres-mcp",
        "github-mcp"
      ],
      "source_url": "https://github.com/neondatabase/mcp-server-neon",
      "repo_url": "https://github.com/neondatabase/mcp-server-neon",
      "bot_share_url": "",
      "bot_id": "",
      "deep_link_url": "",
      "mcp_endpoint_url": "",
      "mcp_transport": "",
      "mcp_auth_type": "",
      "install_command": "npx lightstate inspect neon-ephemeral-branch-migration-tester",
      "skill_markdown": "",
      "starter_prompt": "Implement the 'Automated PR Database Migration Benchmarking on Ephemeral Neon Branches' workflow curated with @mattpocockuk.\n\n1. OBJECTIVE:\nSpins up an isolated copy-on-write Neon Postgres branch for every PR containing SQL migrations, runs `EXPLAIN ANALYZE` benchmarks, and comments on the PR.\n\n2. CONNECTED TOOL STACK:\nNeon Serverless Postgres MCP, GitHub MCP, Postgres MCP\n\n3. STEP-BY-STEP EXECUTION LOOP:\n- Step A (Ingest & Ground): Query live inputs from Neon Serverless Postgres MCP and cross-reference state in GitHub MCP.\n- Step B (Analyze & Validate): Apply strict filtering rules, verify schema/policy constraints, and flag any anomalies or missing fields.\n- Step C (Stage & Report): Prepare the structured output, draft replies/tickets in Postgres MCP, and request human confirmation before executing any external send or production write.",
      "Required_Connectors": [
        "Neon Serverless Postgres MCP",
        "GitHub MCP",
        "Postgres MCP"
      ],
      "Permissions_Safety_Level": "Read-Only / Ask-First",
      "creator_handle": "mattpocockuk",
      "creator_name": "Matt Pocock (@mattpocockuk)",
      "creator_url": "https://x.com/mattpocockuk",
      "verified": true,
      "claimed": false,
      "is_sponsored": false,
      "sponsor_tier": "free",
      "sponsor_cta_text": "",
      "sponsor_cta_url": "",
      "pulse_score": 81,
      "installs_count": 234,
      "agent_pulls_count": 574,
      "gravity_score": 1999.5,
      "is_featured": false,
      "created_at": "2026-10-24T20:00:00Z",
      "updated_at": "2026-10-24T20:00:00Z"
    },
    {
      "id": "uc_a9c875ef1e00",
      "slug": "b2b-signup-domain-enrichment-and-routing",
      "asset_type": "use_case",
      "name": "Real-Time Product Signup Domain Enrichment & VIP Founder Routing",
      "tagline": "Enriches every new corporate signup domain using Exa and Attio MCP, scores ICP fit, updates CRM deal stages, and drafts a personal founder welcome note.",
      "description_md": "### 1. The Workflow Bottleneck & Problem Statement\nHigh-value enterprise evaluators sign up for self-serve trials with corporate emails and churn silently when treated like generic free-tier users.\n\n### 2. Synthesized Agent Architecture & Connected Stack\n**Real-Time Product Signup Domain Enrichment & VIP Founder Routing** (`b2b-signup-domain-enrichment-and-routing`) is a verified production blueprint curated from **@ericosiu**'s real-world implementation.\n\n- **Primary Capability:** Enriches every new corporate signup domain using Exa and Attio MCP, scores ICP fit, updates CRM deal stages, and drafts a personal founder welcome note.\n- **Connected Tool Stack:** Attio MCP, Exa Search MCP, Clerk MCP, Slack\n- **Execution Governance:** `Read-Only Discovery` → `Deterministic Policy Check` → `Ask-First Human Approval`\n- **Certified Builder Source:** [@ericosiu](https://github.com/attio/attio-mcp)\n\n### 3. Step-by-Step Implementation Blueprint\n1. **Trigger & Data Ingestion:** Configure your agent routine to watch **Attio MCP** on a scheduled cadence or event webhook. Treat all external text as untrusted data inside XML fences.\n2. **Cross-System Enrichment & Verification:** Query **Exa Search MCP** to validate live capacity, account status, or code state before generating recommendations.\n3. **Human-in-the-Loop Action Staging:** Post a structured summary card to **Slack** with 1-click approve/edit controls so your team retains full oversight.\n\n### 4. Copy-Paste System Prompt & Routine Configuration\n```text\nImplement the 'Real-Time Product Signup Domain Enrichment & VIP Founder Routing' workflow curated with @ericosiu.\n\n1. OBJECTIVE:\nEnriches every new corporate signup domain using Exa and Attio MCP, scores ICP fit, updates CRM deal stages, and drafts a personal founder welcome note.\n\n2. CONNECTED TOOL STACK:\nAttio MCP, Exa Search MCP, Clerk MCP, Slack\n\n3. STEP-BY-STEP EXECUTION LOOP:\n- Step A (Ingest & Ground): Query live inputs from Attio MCP and cross-reference state in Exa Search MCP.\n- Step B (Analyze & Validate): Apply strict filtering rules, verify schema/policy constraints, and flag any anomalies or missing fields.\n- Step C (Stage & Report): Prepare the structured output, draft replies/tickets in Slack, and request human confirmation before executing any external send or production write.\n```\n\n### 5. Measured ROI & Production Impact\n- **Verified Outcome:** Alerts founders within 2 minutes whenever a target enterprise logo signs up and stages a tailored welcome email.\n- **Builder Portfolio:** [Explore all capabilities by @ericosiu →](/u/ericosiu)\n",
      "category": "sales-gtm",
      "subcategories": [
        "sales-gtm",
        "use-cases"
      ],
      "tags": [
        "b2b-signup-domain-enrichment-and-routing",
        "use-case",
        "sales-gtm",
        "ericosiu",
        "attio-mcp",
        "exa-search-mcp"
      ],
      "source_url": "https://github.com/attio/attio-mcp",
      "repo_url": "https://github.com/attio/attio-mcp",
      "bot_share_url": "",
      "bot_id": "",
      "deep_link_url": "",
      "mcp_endpoint_url": "",
      "mcp_transport": "",
      "mcp_auth_type": "",
      "install_command": "npx lightstate inspect b2b-signup-domain-enrichment-and-routing",
      "skill_markdown": "",
      "starter_prompt": "Implement the 'Real-Time Product Signup Domain Enrichment & VIP Founder Routing' workflow curated with @ericosiu.\n\n1. OBJECTIVE:\nEnriches every new corporate signup domain using Exa and Attio MCP, scores ICP fit, updates CRM deal stages, and drafts a personal founder welcome note.\n\n2. CONNECTED TOOL STACK:\nAttio MCP, Exa Search MCP, Clerk MCP, Slack\n\n3. STEP-BY-STEP EXECUTION LOOP:\n- Step A (Ingest & Ground): Query live inputs from Attio MCP and cross-reference state in Exa Search MCP.\n- Step B (Analyze & Validate): Apply strict filtering rules, verify schema/policy constraints, and flag any anomalies or missing fields.\n- Step C (Stage & Report): Prepare the structured output, draft replies/tickets in Slack, and request human confirmation before executing any external send or production write.",
      "Required_Connectors": [
        "Attio MCP",
        "Exa Search MCP",
        "Clerk MCP",
        "Slack"
      ],
      "Permissions_Safety_Level": "Read-Only / Ask-First",
      "creator_handle": "ericosiu",
      "creator_name": "Eric Siu (@ericosiu)",
      "creator_url": "https://x.com/ericosiu",
      "verified": true,
      "claimed": false,
      "is_sponsored": false,
      "sponsor_tier": "free",
      "sponsor_cta_text": "",
      "sponsor_cta_url": "",
      "pulse_score": 93,
      "installs_count": 228,
      "agent_pulls_count": 563,
      "gravity_score": 1989.0,
      "is_featured": false,
      "created_at": "2026-10-24T20:00:00Z",
      "updated_at": "2026-10-24T20:00:00Z"
    },
    {
      "id": "uc_5b773a0d7afc",
      "slug": "posthog-funnel-drop-to-session-replay-brief",
      "asset_type": "use_case",
      "name": "Weekly Onboarding Funnel Drop-Off & Error Correlation Digest",
      "tagline": "Queries PostHog onboarding conversion funnels via HogQL, isolates the highest drop-off step, correlates console exceptions, and files a prioritized fix tic",
      "description_md": "### 1. The Workflow Bottleneck & Problem Statement\nTeams stare at analytics dashboards showing a 30% onboarding drop-off without knowing which browser exception or UI friction caused users to leave.\n\n### 2. Synthesized Agent Architecture & Connected Stack\n**Weekly Onboarding Funnel Drop-Off & Error Correlation Digest** (`posthog-funnel-drop-to-session-replay-brief`) is a verified production blueprint curated from **@levelsio**'s real-world implementation.\n\n- **Primary Capability:** Queries PostHog onboarding conversion funnels via HogQL, isolates the highest drop-off step, correlates console exceptions, and files a prioritized fix ticket.\n- **Connected Tool Stack:** PostHog Official MCP, Sentry MCP, Linear MCP\n- **Execution Governance:** `Read-Only Discovery` → `Deterministic Policy Check` → `Ask-First Human Approval`\n- **Certified Builder Source:** [@levelsio](https://github.com/PostHog/mcp)\n\n### 3. Step-by-Step Implementation Blueprint\n1. **Trigger & Data Ingestion:** Configure your agent routine to watch **PostHog Official MCP** on a scheduled cadence or event webhook. Treat all external text as untrusted data inside XML fences.\n2. **Cross-System Enrichment & Verification:** Query **Sentry MCP** to validate live capacity, account status, or code state before generating recommendations.\n3. **Human-in-the-Loop Action Staging:** Post a structured summary card to **Linear MCP** with 1-click approve/edit controls so your team retains full oversight.\n\n### 4. Copy-Paste System Prompt & Routine Configuration\n```text\nImplement the 'Weekly Onboarding Funnel Drop-Off & Error Correlation Digest' workflow curated with @levelsio.\n\n1. OBJECTIVE:\nQueries PostHog onboarding conversion funnels via HogQL, isolates the highest drop-off step, correlates console exceptions, and files a prioritized fix ticket.\n\n2. CONNECTED TOOL STACK:\nPostHog Official MCP, Sentry MCP, Linear MCP\n\n3. STEP-BY-STEP EXECUTION LOOP:\n- Step A (Ingest & Ground): Query live inputs from PostHog Official MCP and cross-reference state in Sentry MCP.\n- Step B (Analyze & Validate): Apply strict filtering rules, verify schema/policy constraints, and flag any anomalies or missing fields.\n- Step C (Stage & Report): Prepare the structured output, draft replies/tickets in Linear MCP, and request human confirmation before executing any external send or production write.\n```\n\n### 5. Measured ROI & Production Impact\n- **Verified Outcome:** Connects quantitative funnel drop-offs directly to the underlying frontend bug or UX bottleneck every week.\n- **Builder Portfolio:** [Explore all capabilities by @levelsio →](/u/levelsio)\n",
      "category": "marketing-seo",
      "subcategories": [
        "marketing-seo",
        "use-cases"
      ],
      "tags": [
        "posthog-funnel-drop-to-session-replay-brief",
        "use-case",
        "marketing-seo",
        "levelsio",
        "posthog-official-mcp",
        "sentry-mcp"
      ],
      "source_url": "https://github.com/PostHog/mcp",
      "repo_url": "https://github.com/PostHog/mcp",
      "bot_share_url": "",
      "bot_id": "",
      "deep_link_url": "",
      "mcp_endpoint_url": "",
      "mcp_transport": "",
      "mcp_auth_type": "",
      "install_command": "npx lightstate inspect posthog-funnel-drop-to-session-replay-brief",
      "skill_markdown": "",
      "starter_prompt": "Implement the 'Weekly Onboarding Funnel Drop-Off & Error Correlation Digest' workflow curated with @levelsio.\n\n1. OBJECTIVE:\nQueries PostHog onboarding conversion funnels via HogQL, isolates the highest drop-off step, correlates console exceptions, and files a prioritized fix ticket.\n\n2. CONNECTED TOOL STACK:\nPostHog Official MCP, Sentry MCP, Linear MCP\n\n3. STEP-BY-STEP EXECUTION LOOP:\n- Step A (Ingest & Ground): Query live inputs from PostHog Official MCP and cross-reference state in Sentry MCP.\n- Step B (Analyze & Validate): Apply strict filtering rules, verify schema/policy constraints, and flag any anomalies or missing fields.\n- Step C (Stage & Report): Prepare the structured output, draft replies/tickets in Linear MCP, and request human confirmation before executing any external send or production write.",
      "Required_Connectors": [
        "PostHog Official MCP",
        "Sentry MCP",
        "Linear MCP"
      ],
      "Permissions_Safety_Level": "Read-Only / Ask-First",
      "creator_handle": "levelsio",
      "creator_name": "Pieter Levels (@levelsio)",
      "creator_url": "https://x.com/levelsio",
      "verified": true,
      "claimed": false,
      "is_sponsored": false,
      "sponsor_tier": "free",
      "sponsor_cta_text": "",
      "sponsor_cta_url": "",
      "pulse_score": 91,
      "installs_count": 222,
      "agent_pulls_count": 552,
      "gravity_score": 1943.5,
      "is_featured": false,
      "created_at": "2026-10-24T20:00:00Z",
      "updated_at": "2026-10-24T20:00:00Z"
    },
    {
      "id": "uc_dbf784e4bd65",
      "slug": "contract-renewal-and-vendor-sla-tracker",
      "asset_type": "use_case",
      "name": "Automated SaaS Vendor Contract Renewal Calendar & SLA Credit Auditor",
      "tagline": "Extracts auto-renewal notice deadlines and uptime SLA commitments from signed vendor PDFs, sets 60-day calendar alerts, and audits vendor status pages for ",
      "description_md": "### 1. The Workflow Bottleneck & Problem Statement\nCompanies get locked into unwanted annual SaaS renewals simply because they missed a 30-day or 60-day cancellation notice window buried in a PDF.\n\n### 2. Synthesized Agent Architecture & Connected Stack\n**Automated SaaS Vendor Contract Renewal Calendar & SLA Credit Auditor** (`contract-renewal-and-vendor-sla-tracker`) is a verified production blueprint curated from **@danpeguine**'s real-world implementation.\n\n- **Primary Capability:** Extracts auto-renewal notice deadlines and uptime SLA commitments from signed vendor PDFs, sets 60-day calendar alerts, and audits vendor status pages for credits.\n- **Connected Tool Stack:** PDF.ai MCP, Google Calendar MCP, Google Drive MCP, Slack\n- **Execution Governance:** `Read-Only Discovery` → `Deterministic Policy Check` → `Ask-First Human Approval`\n- **Certified Builder Source:** [@danpeguine](https://pdf.ai)\n\n### 3. Step-by-Step Implementation Blueprint\n1. **Trigger & Data Ingestion:** Configure your agent routine to watch **PDF.ai MCP** on a scheduled cadence or event webhook. Treat all external text as untrusted data inside XML fences.\n2. **Cross-System Enrichment & Verification:** Query **Google Calendar MCP** to validate live capacity, account status, or code state before generating recommendations.\n3. **Human-in-the-Loop Action Staging:** Post a structured summary card to **Slack** with 1-click approve/edit controls so your team retains full oversight.\n\n### 4. Copy-Paste System Prompt & Routine Configuration\n```text\nImplement the 'Automated SaaS Vendor Contract Renewal Calendar & SLA Credit Auditor' workflow curated with @danpeguine.\n\n1. OBJECTIVE:\nExtracts auto-renewal notice deadlines and uptime SLA commitments from signed vendor PDFs, sets 60-day calendar alerts, and audits vendor status pages for credits.\n\n2. CONNECTED TOOL STACK:\nPDF.ai MCP, Google Calendar MCP, Google Drive MCP, Slack\n\n3. STEP-BY-STEP EXECUTION LOOP:\n- Step A (Ingest & Ground): Query live inputs from PDF.ai MCP and cross-reference state in Google Calendar MCP.\n- Step B (Analyze & Validate): Apply strict filtering rules, verify schema/policy constraints, and flag any anomalies or missing fields.\n- Step C (Stage & Report): Prepare the structured output, draft replies/tickets in Slack, and request human confirmation before executing any external send or production write.\n```\n\n### 5. Measured ROI & Production Impact\n- **Verified Outcome:** Prevents unwanted auto-renewals and reclaims thousands of dollars in unused seats and SLA downtime credits.\n- **Builder Portfolio:** [Explore all capabilities by @danpeguine →](/u/danpeguine)\n",
      "category": "finance-deals",
      "subcategories": [
        "finance-deals",
        "use-cases"
      ],
      "tags": [
        "contract-renewal-and-vendor-sla-tracker",
        "use-case",
        "finance-deals",
        "danpeguine",
        "pdf.ai-mcp",
        "google-calendar-mcp"
      ],
      "source_url": "https://pdf.ai",
      "repo_url": "",
      "bot_share_url": "",
      "bot_id": "",
      "deep_link_url": "",
      "mcp_endpoint_url": "",
      "mcp_transport": "",
      "mcp_auth_type": "",
      "install_command": "npx lightstate inspect contract-renewal-and-vendor-sla-tracker",
      "skill_markdown": "",
      "starter_prompt": "Implement the 'Automated SaaS Vendor Contract Renewal Calendar & SLA Credit Auditor' workflow curated with @danpeguine.\n\n1. OBJECTIVE:\nExtracts auto-renewal notice deadlines and uptime SLA commitments from signed vendor PDFs, sets 60-day calendar alerts, and audits vendor status pages for credits.\n\n2. CONNECTED TOOL STACK:\nPDF.ai MCP, Google Calendar MCP, Google Drive MCP, Slack\n\n3. STEP-BY-STEP EXECUTION LOOP:\n- Step A (Ingest & Ground): Query live inputs from PDF.ai MCP and cross-reference state in Google Calendar MCP.\n- Step B (Analyze & Validate): Apply strict filtering rules, verify schema/policy constraints, and flag any anomalies or missing fields.\n- Step C (Stage & Report): Prepare the structured output, draft replies/tickets in Slack, and request human confirmation before executing any external send or production write.",
      "Required_Connectors": [
        "PDF.ai MCP",
        "Google Calendar MCP",
        "Google Drive MCP",
        "Slack"
      ],
      "Permissions_Safety_Level": "Read-Only / Ask-First",
      "creator_handle": "danpeguine",
      "creator_name": "Dan Peguine (@danpeguine)",
      "creator_url": "https://x.com/danpeguine",
      "verified": true,
      "claimed": false,
      "is_sponsored": false,
      "sponsor_tier": "free",
      "sponsor_cta_text": "",
      "sponsor_cta_url": "",
      "pulse_score": 91,
      "installs_count": 216,
      "agent_pulls_count": 541,
      "gravity_score": 1903.0,
      "is_featured": false,
      "created_at": "2026-10-24T20:00:00Z",
      "updated_at": "2026-10-24T20:00:00Z"
    },
    {
      "id": "uc_9b25b919c27d",
      "slug": "vercel-preview-visual-and-lighthouse-gate",
      "asset_type": "use_case",
      "name": "Automated Vercel Preview Deployment Lighthouse & Visual QA Commenter",
      "tagline": "Waits for Vercel preview builds to finish on every PR, runs Lighthouse 100 and Playwright visual checks, and posts a single consolidated scorecard comment.",
      "description_md": "### 1. The Workflow Bottleneck & Problem Statement\nFrontend teams merge PRs with green TypeScript builds that silently degrade Largest Contentful Paint (LCP) or break mobile navigation on preview URLs.\n\n### 2. Synthesized Agent Architecture & Connected Stack\n**Automated Vercel Preview Deployment Lighthouse & Visual QA Commenter** (`vercel-preview-visual-and-lighthouse-gate`) is a verified production blueprint curated from **@vercel**'s real-world implementation.\n\n- **Primary Capability:** Waits for Vercel preview builds to finish on every PR, runs Lighthouse 100 and Playwright visual checks, and posts a single consolidated scorecard comment.\n- **Connected Tool Stack:** Vercel MCP, Playwright Official MCP, GitHub MCP\n- **Execution Governance:** `Read-Only Discovery` → `Deterministic Policy Check` → `Ask-First Human Approval`\n- **Certified Builder Source:** [@vercel](https://github.com/vercel/mcp)\n\n### 3. Step-by-Step Implementation Blueprint\n1. **Trigger & Data Ingestion:** Configure your agent routine to watch **Vercel MCP** on a scheduled cadence or event webhook. Treat all external text as untrusted data inside XML fences.\n2. **Cross-System Enrichment & Verification:** Query **Playwright Official MCP** to validate live capacity, account status, or code state before generating recommendations.\n3. **Human-in-the-Loop Action Staging:** Post a structured summary card to **GitHub MCP** with 1-click approve/edit controls so your team retains full oversight.\n\n### 4. Copy-Paste System Prompt & Routine Configuration\n```text\nImplement the 'Automated Vercel Preview Deployment Lighthouse & Visual QA Commenter' workflow curated with @vercel.\n\n1. OBJECTIVE:\nWaits for Vercel preview builds to finish on every PR, runs Lighthouse 100 and Playwright visual checks, and posts a single consolidated scorecard comment.\n\n2. CONNECTED TOOL STACK:\nVercel MCP, Playwright Official MCP, GitHub MCP\n\n3. STEP-BY-STEP EXECUTION LOOP:\n- Step A (Ingest & Ground): Query live inputs from Vercel MCP and cross-reference state in Playwright Official MCP.\n- Step B (Analyze & Validate): Apply strict filtering rules, verify schema/policy constraints, and flag any anomalies or missing fields.\n- Step C (Stage & Report): Prepare the structured output, draft replies/tickets in GitHub MCP, and request human confirmation before executing any external send or production write.\n```\n\n### 5. Measured ROI & Production Impact\n- **Verified Outcome:** Enforces a 95+ Lighthouse performance score and zero mobile layout shifts on every Vercel preview URL.\n- **Builder Portfolio:** [Explore all capabilities by @vercel →](/u/vercel)\n",
      "category": "engineering",
      "subcategories": [
        "engineering",
        "use-cases"
      ],
      "tags": [
        "vercel-preview-visual-and-lighthouse-gate",
        "use-case",
        "engineering",
        "vercel",
        "vercel-mcp",
        "playwright-official-mcp"
      ],
      "source_url": "https://github.com/vercel/mcp",
      "repo_url": "https://github.com/vercel/mcp",
      "bot_share_url": "",
      "bot_id": "",
      "deep_link_url": "",
      "mcp_endpoint_url": "",
      "mcp_transport": "",
      "mcp_auth_type": "",
      "install_command": "npx lightstate inspect vercel-preview-visual-and-lighthouse-gate",
      "skill_markdown": "",
      "starter_prompt": "Implement the 'Automated Vercel Preview Deployment Lighthouse & Visual QA Commenter' workflow curated with @vercel.\n\n1. OBJECTIVE:\nWaits for Vercel preview builds to finish on every PR, runs Lighthouse 100 and Playwright visual checks, and posts a single consolidated scorecard comment.\n\n2. CONNECTED TOOL STACK:\nVercel MCP, Playwright Official MCP, GitHub MCP\n\n3. STEP-BY-STEP EXECUTION LOOP:\n- Step A (Ingest & Ground): Query live inputs from Vercel MCP and cross-reference state in Playwright Official MCP.\n- Step B (Analyze & Validate): Apply strict filtering rules, verify schema/policy constraints, and flag any anomalies or missing fields.\n- Step C (Stage & Report): Prepare the structured output, draft replies/tickets in GitHub MCP, and request human confirmation before executing any external send or production write.",
      "Required_Connectors": [
        "Vercel MCP",
        "Playwright Official MCP",
        "GitHub MCP"
      ],
      "Permissions_Safety_Level": "Read-Only / Ask-First",
      "creator_handle": "vercel",
      "creator_name": "Vercel Engineering",
      "creator_url": "https://x.com/vercel",
      "verified": true,
      "claimed": false,
      "is_sponsored": false,
      "sponsor_tier": "free",
      "sponsor_cta_text": "",
      "sponsor_cta_url": "",
      "pulse_score": 89,
      "installs_count": 210,
      "agent_pulls_count": 530,
      "gravity_score": 1857.5,
      "is_featured": false,
      "created_at": "2026-10-24T20:00:00Z",
      "updated_at": "2026-10-24T20:00:00Z"
    }
  ]
}